diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..dd84ea7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,38 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..bbcbbe7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/codeql-analysis.yml b/.github/workflows/codeql-analysis.yml new file mode 100644 index 0000000..6a43392 --- /dev/null +++ b/.github/workflows/codeql-analysis.yml @@ -0,0 +1,71 @@ +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL" + +on: + push: + branches: [ master ] + pull_request: + # The branches below must be a subset of the branches above + branches: [ master ] + schedule: + - cron: '27 16 * * 1' + +jobs: + analyze: + name: Analyze + runs-on: ubuntu-latest + permissions: + actions: read + contents: read + security-events: write + + strategy: + fail-fast: false + matrix: + language: [ 'cpp', 'python' ] + # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ] + # Learn more: + # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v1 + with: + languages: ${{ matrix.language }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + # queries: ./path/to/local/query, your-org/your-repo/queries@main + + # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v1 + + # ℹī¸ Command-line programs to run using the OS shell. + # 📚 https://git.io/JvXDl + + # ✏ī¸ If the Autobuild fails above, remove it and uncomment the following three lines + # and modify them (or add more) to build your code if your project + # uses a compiled language + + #- run: | + # make bootstrap + # make release + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v1 diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index d00fbdb..ac53865 --- a/.gitignore +++ b/.gitignore @@ -37,3 +37,15 @@ .Trashes ehthumbs.db Thumbs.db +_gsdata_/* + +# UPGRADE STUDY # +###################### +# We want to exclude *input* and *output* images (i.e. tiles, NDPI, TIFs); +# these are 1) personal data, and 2) too large. +upgrade_study/* +upgrade_study/_temp/* +upgrade_study/CD68/* +upgrade_study/EVG/* +upgrade_study/SMA/* +.DS_Store diff --git a/LICENSE b/LICENSE index 70822f7..f736b8e 100755 --- a/LICENSE +++ b/LICENSE @@ -1,5 +1,8 @@ The MIT License (MIT) -Copyright (c) 2014-2015, UMC Utrecht, Bas Nelissen +Copyright (c) 2014-2021, +Bas G.L. Nelissen (https://github.com/bglnelissen), +Sander W. van der Laan (https://github.com/swvanderlaan), +UMC Utrecht, Utrecht, the Netherlands. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the @@ -18,4 +21,4 @@ MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE -SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/README.md b/README.md index 1b5fe6d..fb6932f 100755 --- a/README.md +++ b/README.md @@ -1,22 +1,39 @@ -[slideToolkit](http://bglnelissen.github.io/slideToolkit) +[slideToolKit](https://github.com/swvanderlaan/slideToolKit) ============ -####_slideToolkit: an assistive toolset for the histological quantification of whole slide images_ +**slideToolKit**: _an assistive toolset for the histological quantification of whole slide images_ The demand for accurate and reproducible phenotyping of a disease trait increases with the rising number of biobanks and genome wide association studies. Detailed analysis of histology is a powerful way of phenotyping human tissues. Nonetheless, purely visual assessment of histological slides is time-consuming and liable to sampling variation and optical illusions and thereby observer variation, and external validation may be cumbersome. Therefore, computerized quantification of digitized histological slides is often preferred as a more precise and reproducible, and sometimes more sensitive approach. Relatively few free toolkits are, however, available for fully digitized microscopic slides, usually known as whole slides images. -In order to comply with this need, we developed the slideToolkit as a fast method to handle large quantities of low contrast whole slides images using advanced cell detecting algorithms. The slideToolkit has been developed for modern personal computers and high-performance clusters (HPCs) and is available as an open-source project on github.com. +In order to comply with this need, we developed the **slideToolKit** as a fast method to handle large quantities of low contrast whole slides images using advanced cell detecting algorithms. At the core **slideToolKit** uses [CellProfiler](http://cellprofiler.org) to analyze the whole slide images efficiently. The **slideToolKit** has been developed for modern personal computers (macOS, Ubuntu) and high-performance clusters (HPCs) and is available as an open-source project on [GitHub](https://swvanderlaan.github.io/slideToolKit/). -A workflow consists of four consecutive steps. In the first step (acquisition), whole slide images are collected and converted to TIFF files. In the second step (preparation), files are organized. The third step (tiles), creates multiple manageable tiles to count. In the fourth step (analysis), tissue is analyzed and results are stored in a data set. Using this method, two consecutive measurements of 303 slides showed an intraclass correlation of 0.99. +A typical [workflow](images/slideToolkit.workflow.png) consists of four consecutive steps. +- In the first step (acquisition), whole slide images are collected and converted to TIFF files. +- In the second step (preparation), files are organized. +- The third step (tiles), creates multiple manageable tiles to count. +- In the fourth step (analysis), tissue is analyzed and results are stored in a data set. Using this method, two consecutive measurements of 303 slides showed an intraclass correlation of 0.99 ([Vrijenhoek J.E.P. _et al._](https://www.ncbi.nlm.nih.gov/pubmed/25541691)). -In conclusion, slideToolkit provides a free, powerful and versatile collection of tools for automated feature analysis of whole slide images to create reproducible and meaningful phenotypic datasets. ------------------------------------------------ -Citation: Nelissen BGL, van Herwaarden JA, Moll FL, van Diest PJ, Pasterkamp G (2014) SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images. PLoS ONE 9(11): e110289. doi:10.1371/journal.pone.0110289. -Direct link: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110289. + + +_The slideToolKit workflow._ + +In conclusion, **slideToolKit** provides a free, powerful and versatile collection of tools for automated feature analysis of whole slide images to create reproducible and meaningful phenotypic datasets. + + +#### Instructions and usage +Detailed instructions on using **slideToolKit** can be found in the [wiki](https://github.com/swvanderlaan/slideToolKit/wiki). + + +#### Citations +- Nelissen B.G.L., van Herwaarden J.A., Moll F.L., van Diest P.J., and Pasterkamp G. (2014) _SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images._ **PLoS One.** 2014 Nov 5;9(11):e110289. doi: 10.1371/journal.pone.0110289. [Direct link](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0110289). +- Vrijenhoek J.E.P., Nelissen B.G.L., Velema E., Vons K., de Vries J.P.P.M., Eijkemans M.J.C., den Ruijter H.M., de Borst G.J., Moll F.L., and Pasterkamp G (2014) _High Reproducibility of Histological Characterization by Whole Virtual Slide Quantification; An Example Using Carotid Plaque Specimens._ **PLoS One**. 2014 Dec 26;9(12):e115907. doi: 10.1371/journal.pone.0115907. [Direct link](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0115907). +- Mekke J.M., Sakkers T.R., Verwer M.C., van den Dungen N.A.M, Song Y., Miller C., Pasterkamp G., Mokry M., den Ruijter H.M., de Kleijn D.P.V., de Borst G.J., Haitjema S., and van der Laan S.W. _Glycophorin C in atherosclerotic plaque is associated with major adverse cardiovascular events after carotid endarterectomy_ preprint **medRxiv** 2021.07.15.21260570; doi: [https://doi.org/10.1101/2021.07.15.21260570](https://doi.org/10.1101/2021.07.15.21260570) -The MIT License (MIT): . -Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. ----------------------------------------------- -> Installation instructions for Ubuntu and Mac OS X Mountain Lion+ can be found in [guides](guides). +#### Licence +The MIT License (MIT): . + +Copyright (c) 2014-2021, [Bas G.L. Nelissen](https://github.com/bglnelissen) & [Sander W. van der Laan](https://github.com/swvanderlaan), UMC Utrecht, Utrecht, the Netherlands. + diff --git a/conda_yml/anaconda3_8_2021_05_cp413.v2.yml b/conda_yml/anaconda3_8_2021_05_cp413.v2.yml new file mode 100644 index 0000000..4d79f13 --- /dev/null +++ b/conda_yml/anaconda3_8_2021_05_cp413.v2.yml @@ -0,0 +1,51 @@ +# run: conda env create -f anaconda3_8_2021_05_cp413.v2.yml +# run: conda env update -f anaconda3_8_2021_05_cp413.v2.yml +# run: conda env remove -n cp4 +name: cp4 + +channels: +- conda-forge +- anaconda +- bioconda +- defaults + +dependencies: +- python=3.8 +- pip +- numpy +- matplotlib +- pandas +- mysqlclient +- openjdk +- scikit-learn +- mahotas +- gtk2 +- Jinja2=3.0.1 +- inflect=5.3.0 +- wxpython=4.1.0 +- mysqlclient=1.4.4 +- sentry-sdk=0.18.0 +- bftools +- cairo +- freetype +- gettext +- giflib +- gtk2 +- gtk3 +- imagemagick +- java-jdk +- jpeg +- wmctrl +- zbar +- tclap +- pip: + - cellprofiler==4.1.3 + - arrow + - pathlib + - opencv-contrib-python + - openslide-python>=1.1.2 + +# you could automatically provide the prefix, or just do it via the prompt +# prefix: ~/anaconda3/envs/cp4 + +# prefix: /Users/[username]/anaconda3/envs/cp4 diff --git a/conda_yml/conda3_8_cp413.v1.yml b/conda_yml/conda3_8_cp413.v1.yml new file mode 100644 index 0000000..fd3cdf8 --- /dev/null +++ b/conda_yml/conda3_8_cp413.v1.yml @@ -0,0 +1,51 @@ +# run: conda env create -f conda3_8_cp413.v1.yml +# run: conda env update -f conda3_8_cp413.v1.yml +# run: conda env remove -n cp4 +name: cp4 + +channels: +- conda-forge +- miniconda +- bioconda +- defaults + +dependencies: +- python=3.8 +- pip +- numpy +- matplotlib +- pandas +- mysqlclient +- openjdk +- scikit-learn +- mahotas +- gtk2 +- Jinja2=3.0.1 +- inflect=5.3.0 +- wxpython=4.1.0 +- mysqlclient=1.4.4 +- sentry-sdk=0.18.0 +- bftools +- cairo +- freetype +- gettext +- giflib +- gtk2 +- gtk3 +- imagemagick +- java-jdk +- jpeg +- wmctrl +- zbar +- tclap +- pip: + - cellprofiler==4.1.3 + - arrow + - pathlib + - opencv-contrib-python + - openslide-python>=1.1.2 + +# you could automatically provide the prefix, or just do it via the prompt +# prefix: ~/miniconda3/envs/cp4 + +# prefix: /Users/[username]/miniconda3/envs/cp4 diff --git a/examples/README.md b/examples/README.md index b78b082..3da99a7 100755 --- a/examples/README.md +++ b/examples/README.md @@ -1,7 +1,7 @@ -[Examples](http://bglnelissen.github.io/slideToolkit) +[Examples](http://swvanderlaan.github.io/slideToolkit) ============ -An example pipeline made with CellProfiler is provided. +A couple of example pipelines made with CellProfiler are provided. ----------------------------------------------- > Installation instructions can be found in [guides](guides). \ No newline at end of file diff --git a/guides/INSTALL.OSX.md b/guides/INSTALL.OSX.md deleted file mode 100755 index 5b34886..0000000 --- a/guides/INSTALL.OSX.md +++ /dev/null @@ -1,157 +0,0 @@ -macOS and OS X 10.9+ - slideToolkit installation instructions -============ - -The slideToolkit is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your current slideToolkit installation. - -We have tested slideToolkit on CentOS6, CentOS7, OS X Mountain Lion (version 10.8.[x]), and macOS Sierra (version 10.12.[x]). - -Please tell us if you run into problems, it is likely we can help you out - we have done this before. ;) - --------------- - -#### Some installation basics - -We tried to create as few steps as possible with one-liners that are *easy* to read. Most of the installation is done using the commandline. You can copy/paste each example command, per block of code. For some steps you need administrator privileges. Follow the steps in consecutive order. - -``` -these `mono-type font` illustrate commands illustrate terminal commands. You can copy & paste these. -``` - -To make it easier to copy and paste, long commands that stretch over multiple lines are structered as follows: - -``` -Multiline commands end with a dash \ - indent 4 spaces, and continue on the next line. \ - Copy & paste these whole blocks of code. -``` - -Although we made it easy to just select, copy and paste and run these blocks of code, it is not a good practise to blindly copy and paste commands. Try to be aware about what you are doing. And never, never run `sudo` commands without a good reason to do so. - --------------- - -#### Step 1 - Update and prepare -The system must be up-to-date. Go to the Apple menu on the top left, click "Software Update...", and click the "Update all" button. If the system asks you if you want to turn on automatic updates, select 'Turn on'. Restart your system if needed. - -Binairies are executed from your local `bin` folder. By default this folder does not exists and is not present in your PATH. Create your `~/bin` and add it to your PATH if needed. - -``` -if ! [[ "$PATH" =~ (:$HOME/bin:|:~/bin:) ]] ; then \ - mkdir -p ~/bin && \ - printf "\n# Add ~/bin to your PATH\nexport PATH=\"~/bin:\$PATH\" \n" >> ~/.bash_profile - fi -``` - -Now we are up to date, and ready to continue the installation. - -#### Step 2 - Install XQuartz, a version of the X.Org X Window System that runs on OS X -XQuartz is needed. Go to [xquartz.macosforge.org](http://xquartz.macosforge.org), download and install the latest stable version of XQuartz. You can find it under "Quick Download". - -On the end of the installation you are asked to log out and log back in, and of course you comply. - -#### Step 3 - Install brew đŸē, the missing package manager for OS X -We install [brew](http://brew.sh) using the following one-liner. You need administrator rights for the installation. No characters will appear on the screen when you type in your password. - -``` -/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" -``` -P.S. You can check out more handy Homebrew repositories here: https://github.com/Homebrew. - -When asked if you want to install the "command line developer tools", choose 'Install'. After the "command line developer tools" installation, continue the installation in the Terminal - -After a `brew` installation, it is wise to do a `brew doctor` and follow the instructions on the screen. - -``` -brew doctor -``` -A final update and upgrade for your `brew` packages - -``` -brew update && brew upgrade -``` - -From now on, we asume your `brew` package manager is good to go. - -#### Step 4 - Install required libraries and packages using brew đŸē -We install most packages using brew. - -``` -brew install automake wget jpeg libpng libtiff parallel openslide zbar -``` -We need to install 'wmctrl' (https://linux.die.net/man/1/wmctrl) in a slightly different way. -``` -brew install homebrew/x11/wmctrl -``` -Uninstall previous installations of imagemagick first before we build it from source, and it with the extra libraries. - -``` -brew uninstall --force imagemagick && \ - brew install imagemagick --with-libpng --with-libtiff --with-x11 --build-from-source -``` - -#### Step 5 - Install the bioformat tools -Install the latest version of BioFormats, including `bfconvert`. - -``` -mkdir -p ~/usr -``` -``` -cd ~/usr && wget http://downloads.openmicroscopy.org/latest/bio-formats5/artifacts/bftools.zip && \ - unzip -o bftools.zip && \ - rm bftools.zip -``` -Add symbolic links in `~/bin/`. Now the BioFormats tools will be availabe in your PATH. Adding the bftools to your PATH is obligatory for the slideToolkit to find its dependencies. - -``` -mkdir -p ~/bin/ && ln -s -f -v ~/usr/bftools/bfconvert ~/bin/ && \ - ln -s -f -v ~/usr/bftools/domainlist ~/bin/ && \ - ln -s -f -v ~/usr/bftools/formatlist ~/bin/ && \ - ln -s -f -v ~/usr/bftools/ijview ~/bin/ && \ - ln -s -f -v ~/usr/bftools/mkfake ~/bin/ && \ - ln -s -f -v ~/usr/bftools/showinf ~/bin/ && \ - ln -s -f -v ~/usr/bftools/tiffcomment ~/bin/ && \ - ln -s -f -v ~/usr/bftools/xmlindent ~/bin/ && \ - ln -s -f -v ~/usr/bftools/xmlvalid ~/bin/ -``` - -#### Step 6 - Install datamatrix barcode libraries -Install the latest version of libdmtx, including `dmtxread`. First we install the libraries: - -``` -brew install libdmtx dmtx-utils -``` - -The dmtx and libdmtx binairies are installed in `/usr/local/bin`. This is the folder `brew` uses for its installations and should already be in your PATH. - -#### Step 7 - Install slideToolkit -Download and install the latest version of the slideToolkit from github. First create and go to the git directory, then download the slideToolkit. - -``` -mkdir -p ~/git/ && cd ~/git -``` -``` -if [ -d ~/git/slideToolkit/.git ]; then \ - cd ~/git/slideToolkit && git pull; \ - else \ - cd ~/git/ && git clone https://github.com/swvanderlaan/slideToolkit.git; \ - fi -``` - -Add symbolic links in `~/bin/`. Now the slideToolkit will be availabe in your PATH. Adding the slideToolkit tools to your PATH makes it easier to acces the slideToolkit commands. - -``` -mkdir -p ~/bin/ && ln -s -f -v ~/git/slideToolkit/slide* ~/bin/ -``` - -#### Step 8 - Install CellProfiler -Install CellProfiler following instructions on their [website](http://cellprofiler.org/download.shtml). Using the downloaded installer, CellProfiler will be installed in the default location (/Applications/CellProfiler). - -To make the CellProfiler command line interface (CLI) available, we create a `cellprofiler` script in your `~/bin` folder. 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images/slideToolkit.workflow.tif diff --git a/legacy/README.md b/legacy/README.md new file mode 100755 index 0000000..6846d0b --- /dev/null +++ b/legacy/README.md @@ -0,0 +1,8 @@ +Legacy +============ + +These scripts are legacy. + +- `slideQuantifySGE` is the original `slideQuantify` script that uses the `qsub` system (`SGE`) for job submission +- `slideQuantifyDev` is the original `slideQuantify` script based on `SGE` that was used for development + diff --git a/legacy/anaconda3_8_2021_05_cp413.v1.yml b/legacy/anaconda3_8_2021_05_cp413.v1.yml new file mode 100644 index 0000000..792e249 --- /dev/null +++ b/legacy/anaconda3_8_2021_05_cp413.v1.yml @@ -0,0 +1,207 @@ +# run: conda env create -f anaconda3_8_2021_05_cp413.yml +# run: conda env update -f anaconda3_8_2021_05_cp413.yml +# run: conda env remove -n cp4 +name: cp4 + +channels: +- conda-forge +- anaconda +- bioconda +- defaults + +dependencies: +- python=3.8 +- pip +- numpy +- matplotlib +- pandas +- mysqlclient +- openjdk +- scikit-learn +- mahotas +- gtk2 +- Jinja2=3.0.1 +- inflect=5.3.0 +- wxpython=4.1.0 +- mysqlclient=1.4.4 +- sentry-sdk=0.18.0 +#- atk-1.0=2.36.0=he69c4ee_4 +#- attrs=21.2.0=pyhd8ed1ab_0 +#- autoconf=2.69=pl526he370928_5 +#- automake=1.16.2=pl526_0 +#- backports=1.0=py_2 +#- backports.lzma=0.0.14=py38hba84461_3 +- bftools #=5.7.1=hdfd78af_1 +#- blas=1.0=openblas +#- brotlipy=0.7.0=py38h96a0964_1001 +#- bzip2=1.0.8=h1de35cc_0 +#- ca-certificates=2021.7.5=hecd8cb5_1 +- cairo #=1.16.0=he43a7df_1008 +#- certifi=2021.5.30=py38hecd8cb5_0 +#- cffi=1.14.6=py38h9688ba1_0 +#- chardet=4.0.0=py38h50d1736_1 +#- charset-normalizer=2.0.4=pyhd3eb1b0_0 +#- cmake=3.14.0=haff7e42_0 +#- cryptography=3.4.7=py38h1fa4640_0 +#- cycler=0.10.0=py_2 +#- cython=0.29.21=py38hb1e8313_0 +#- enum34=1.1.10=py38hecd8cb5_0 +#- epoxy=1.5.9=h0d85af4_0 +#- expat=2.4.1=he49afe7_0 +#- fftw=3.3.9=nompi_h02cd531_101 +#- font-ttf-dejavu-sans-mono=2.37=hab24e00_0 +#- font-ttf-inconsolata=3.000=h77eed37_0 +#- font-ttf-source-code-pro=2.038=h77eed37_0 +#- font-ttf-ubuntu=0.83=hab24e00_0 +#- fontconfig=2.13.1=h10f422b_1005 +#- fonts-conda-ecosystem=1=0 +#- fonts-conda-forge=1=0 +- freetype #=2.10.4=ha233b18_0 +#- fribidi=1.0.10=hbcb3906_0 +#- gdk-pixbuf=2.42.6=h2e6141f_0 +- gettext #=0.19.8.1=h7937167_1005 +#- ghostscript=9.54.0=he49afe7_1 +- giflib #=5.2.1=hbcb3906_2 +#- glib-tools=2.68.4=he49afe7_0 +#- graphite2=1.3.13=h2e338ed_1001 +#- graphviz=2.49.0=h77de9ca_0 +- gtk2 #=2.24.33=h675d97a_1 +- gtk3 #=3.24.29=h9499984_1 +#- gts=0.7.6=hccb3bdf_2 +#- harfbuzz=2.9.0=h159f659_0 +#- hicolor-icon-theme=0.17=h694c41f_2 +#- icu=68.1=h74dc148_0 +#- idna=2.10=pyh9f0ad1d_0 +- imagemagick #=7.0.11_14=pl5320h3e49128_0 +#- inflect=5.3.0=py38h50d1736_0 +- java-jdk #=8.0.92=1 +#- jbig=2.1=h0d85af4_2003 +#- jinja2=3.0.1=pyhd8ed1ab_0 +#- joblib=1.0.1=pyhd8ed1ab_0 +- jpeg #=9=hbcb3906_0 +#- kiwisolver=1.3.2=py38h12bbefe_0 +#- krb5=1.18.2=h75d18d8_0 +#- lcms2=2.12=h577c468_0 +#- lerc=2.2.1=h046ec9c_0 +#- libblas=3.9.0=11_osx64_openblas +#- libcblas=3.9.0=11_osx64_openblas +#- libcurl=7.71.1=h8a08a2b_1 +#- libcxx=12.0.1=habf9029_0 +#- libdeflate=1.7=h35c211d_5 +#- libedit=3.1.20191231=h1de35cc_1 +#- libffi=3.3=h046ec9c_2 +#- libgd=2.3.2=h4e7a7ea_0 +#- libgfortran=5.0.0=9_3_0_h6c81a4c_23 +#- libgfortran5=9.3.0=h6c81a4c_23 +#- libglib=2.68.4=hd556434_0 +#- libiconv=1.16=haf1e3a3_0 +#- libjpeg-turbo=2.1.0=h0d85af4_0 +#- liblapack=3.9.0=11_osx64_openblas +#- libopenblas=0.3.17=openmp_h3351f45_1 +#- libpng=1.6.37=ha441bb4_0 +#- librsvg=2.50.7=hd2a7919_0 +#- libssh2=1.9.0=ha12b0ac_1 +#- libtiff=4.3.0=h1167814_1 +#- libtool=2.4.6=h2e338ed_1007 +#- libwebp=1.2.1=h28dabe5_0 +#- libwebp-base=1.2.1=h0d85af4_0 +#- libxcb=1.13=h35c211d_1003 +#- libxml2=2.9.12=h93ec3fd_0 +#- llvm-openmp=12.0.1=hda6cdc1_1 +#- lz4-c=1.9.3=he49afe7_1 +#- m4=1.4.18=h1de35cc_1 +#- mahotas=1.4.11=py38he9f00de_1 +#- markupsafe=2.0.1=py38h96a0964_0 +#- mysql-connector-c=6.1.11=h0f02589_1007 +#- ncurses=6.2=h2e338ed_4 +#- numpy=1.19.1=py38ha6d8c68_0 +#- numpy-base=1.19.1=py38h68fea81_0 +#- olefile=0.46=pyh9f0ad1d_1 +#- openjdk=11.0.9.1=hcf210ce_1 +#- openjpeg=2.4.0=h6e7aa92_1 +#- openssl=1.1.1k=h0d85af4_1 +#- pandas=1.3.2=py38ha53d530_0 +#- pango=1.48.9=ha05cd14_0 +#- parallel=20210822=h694c41f_0 +#- pathlib2=2.3.6=py38h50d1736_0 +#- pcre=8.45=he49afe7_0 +#- perl=5.26.2=h4e221da_0 +#- pillow=8.3.1=py38hee640a0_0 +#- pip=21.2.4=pyhd8ed1ab_0 +#- pixman=0.40.0=hbcb3906_0 +#- pkg-config=0.29.2=h31203cd_1008 +#- pthread-stubs=0.4=hc929b4f_1001 +#- pycparser=2.20=pyh9f0ad1d_2 +#- pylibdmtx=0.1.9=pyh9f0ad1d_0 +#- pyopenssl=20.0.1=pyhd8ed1ab_0 +#- pyparsing=2.4.7=pyh9f0ad1d_0 +#- pypubsub=4.0.3=py_0 +#- pysocks=1.7.1=py38h50d1736_3 +#- python=3.8.10=h0e5c897_0_cpython +#- python-dateutil=2.8.2=pyhd8ed1ab_0 +#- python.app=1.3=py38h96a0964_5 +#- python_abi=3.8=2_cp38 +#- pytz=2021.1=pyhd8ed1ab_0 +#- readline=8.1=h05e3726_0 +#- requests=2.26.0=pyhd3eb1b0_0 +#- rhash=1.4.0=hddc9c9b_0 +#- scikit-learn=0.24.2=py38h37f3bb3_1 +#- scipy=1.7.1=py38hd329d04_0 +#- sentry-sdk=0.18.0=pyh9f0ad1d_0 +#- setuptools=57.4.0=py38h50d1736_0 +#- six=1.16.0=pyh6c4a22f_0 +#- sqlite=3.36.0=h23a322b_0 +#- threadpoolctl=2.2.0=pyh8a188c0_0 +#- tk=8.6.11=hd798d34_0 +#- tornado=6.1=py38h96a0964_1 +#- urllib3=1.26.6=pyhd8ed1ab_0 +#- wheel=0.37.0=pyhd8ed1ab_1 +- wmctrl #=0.4=pyhd8ed1ab_0 +#- wxpython=4.1.0=py38haae4e1a_4 +#- xorg-kbproto=1.0.7=h35c211d_1002 +#- xorg-libice=1.0.10=h0d85af4_0 +#- xorg-libsm=1.2.3=h0d85af4_1000 +#- xorg-libx11=1.7.2=h0d85af4_0 +#- xorg-libxau=1.0.9=h35c211d_0 +#- xorg-libxdmcp=1.1.3=h35c211d_0 +#- xorg-libxext=1.3.4=h0d85af4_1 +#- xorg-libxrender=0.9.10=h0d85af4_1003 +#- xorg-libxt=1.2.1=h0d85af4_2 +#- xorg-renderproto=0.11.1=h0d85af4_1002 +#- xorg-xextproto=7.3.0=h35c211d_1002 +#- xorg-xproto=7.0.31=h35c211d_1007 +#- xz=5.2.5=haf1e3a3_1 +- zbar #=0.10=h0a44026_1000 +#- zlib=1.2.11=h1de35cc_3 +#- zstd=1.5.0=h582d3a0_0 +- pip: + - cellprofiler==4.1.3 +# - boto3==1.18.32 +# - botocore==1.21.32 +# - cellprofiler==4.1.3 +# - cellprofiler-core==4.1.3 +# - centrosome==1.2.0 +# - deprecation==2.1.0 +# - docutils==0.15.2 +# - future==0.18.2 +# - h5py==2.10.0 +# - imageio==2.9.0 +# - jmespath==0.10.0 +# - matplotlib==3.1.3 +# - mysqlclient==1.4.6 +# - networkx==2.6.2 +# - packaging==21.0 +# - prokaryote==2.4.2 +# - psutil==5.8.0 +# - python-bioformats==4.0.4 +# - python-javabridge==4.0.3 +# - pywavelets==1.1.1 +# - pyzmq==18.0.1 +# - s3transfer==0.5.0 +# - scikit-image==0.18.3 +# - tifffile==2021.8.30 + +# you could automatically provide the prefix, or just do it via the prompt +# prefix: ~/anaconda3/envs/cp4 + +# prefix: /Users/[username]/anaconda3/envs/cp4 diff --git a/guides/INSTALL.CentOS7.md b/legacy/guides/INSTALL.CentOS7.admin.md similarity index 92% rename from guides/INSTALL.CentOS7.md rename to legacy/guides/INSTALL.CentOS7.admin.md index 42afff9..870afc5 100755 --- a/guides/INSTALL.CentOS7.md +++ b/legacy/guides/INSTALL.CentOS7.admin.md @@ -1,4 +1,4 @@ -CentOS 7 ~ HPC-version - slideToolkit installation instructions +CentOS 7 ~ administrator-version - slideToolkit installation instructions ============ The slideToolkit is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your current slideToolkit installation. @@ -24,8 +24,12 @@ Although we made it easy to just select, copy and paste and run these blocks of NOTE: these instructions are for CentOS7 on a high-performance computer cluster, such as the HPC at the UMC Utrecht. ------------ +## Administrator version -#### Update and prepare +> Note: the instructions below require **full** adminstration rights. + + +### Update and prepare To make installing easier, we will add the current user to the sudoers file, this makes it possible to run `sudo`. Replace USERNAME with your username. ``` @@ -49,7 +53,7 @@ if ! [[ "$PATH" =~ ($HOME/bin:|~/bin:) ]] ; then \ Now we are up to date, and ready to continue the installation. -#### Install required libraries and packages using apt-get +### Install required libraries and packages using apt-get Let's first check whether some packages are installed already. ``` @@ -67,12 +71,22 @@ su -c 'yum -y install autoconf automake cmake gcc-c++ \ gimp openjpeg giflib-devel libjpeg-devel libtiff-devel libpng-devel freetype-devel' ``` -# wmctrl -# zbar-tools +### Install `wmctrl` +Install the latest version of `wmctrl`. + +``` +yum install wmctrl +``` + +### Install `zbar` +Install the latest version of `zbar`. +``` +yum install zbar +``` -#### Install parallel -Install the latest version of GNU Parallel. First create and go to the src directory, then download and extract parallel. +### Install `parallel` +Install the latest version of GNU Parallel. First create and go to the src directory, then download and extract `parallel`. ``` mkdir -p ~/src && cd ~/src && \\ @@ -90,8 +104,8 @@ cd ~/src/parallel-* ./configure && make && su -c "make install" && make clean ``` -#### Install zlib -Install the latest zlib compression libraries. First create and go to the src directory, then download and extract zlib. +#### Install `zlib` +Install the latest `zlib` compression libraries. First create and go to the src directory, then download and extract `zlib`. ``` mkdir -p ~/src && cd ~/src && \\ @@ -274,7 +288,7 @@ mkdir -p ~/git/ && cd ~/git if [ -d ~/git/slideToolkit/.git ]; then \ cd ~/git/slideToolkit && git pull; \ else \ - cd ~/git/ && git clone https://github.com/bglnelissen/slideToolkit.git; \ + cd ~/git/ && git clone https://github.com/swvanderlaan/slideToolKit.git; \ fi ``` diff --git a/legacy/guides/INSTALL.CentOS7.md b/legacy/guides/INSTALL.CentOS7.md new file mode 100755 index 0000000..0422ab7 --- /dev/null +++ b/legacy/guides/INSTALL.CentOS7.md @@ -0,0 +1,248 @@ +CentOS 7 ~ Anaconda-version - slideToolkit installation instructions +============ + +The slideToolkit is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your current slideToolkit installation. + +Please tell us if you run into problems, it is likely we can help you out, we have done this before ;) + +I tried to create as few steps as possible with one-liners that are *easy* to read. Most of the installation is done using the commandline. You can copy/paste each example command, per block of code. For some steps you need administrator privileges. Follow the steps in consecutive order. + +``` +these `mono-type font` illustrate commands illustrate terminal commands. You can copy & paste these. +``` + +To make it easier to copy and paste, long commands that stretch over multiple lines are structed as follows: + +``` +Multiline commands end with a dash \ + indent 4 spaces, and continue on the next line. \ + Copy & paste these whole blocks of code. +``` + +Although we made it easy to just select, copy and paste and run these blocks of code, it is not a good practise to blindly copy and paste commands. Try to be aware about what you are doing. And never, never run `sudo` commands without a good reason to do so. + +NOTE: these instructions are for CentOS7 on a high-performance computer cluster, such as the HPC at the UMC Utrecht. + +------------ + +## Anaconda version + +The HPC is managed and user do not normally have admin rights. Therefore it is advised to install a distribution of `anaconda`. + +### Step 1: check what is available on the system +Let's first check whether some packages are installed already. + +``` +for PACK in curl cvs gcc gcc-c++ gimp git libtool openjpeg perl svn vim wget giflib-devel libjpeg-devel libtiff-devel libpng-devel freetype-devel; do echo "* checking [ "$PACK" ]...."; command -v "$PACK"; echo "---------"; echo ""; done +``` + +### Step 2: installation anaconda +We require `anaconda` to have full control on the installation of required libraries and packages for `slideToolKit`. + +If `anaconda` is available, make sure it is up-to-date. + +``` +conda update -n base conda +``` + +If `anaconda` is not available, you can grab a [link here](https://www.anaconda.com/products/individual#linux). Next, execute the following two lines in the root folder where you want `anaconda` to be installed, e.g. `/hpc/local/CentOS7/dhl_ec/software/`: + +``` +wget https://repo.anaconda.com/archive/Anaconda3-2021.05-Linux-x86_64.sh +``` + +``` +bash Anaconda3-2021.05-Linux-x86_64.sh +``` + +And follow the instructions. + +If you don't want conda to be loaded on startup, you can execute the following command. + +``` +conda config --set auto_activate_base false +``` + +And don't forget to cleanup afterwards. + +``` +rm -v Anaconda3-2021.05-Linux-x86_64.sh +``` + +### Step 4: make a module for anaconda + +You can create a [`modulefile`](https://lmod.readthedocs.io/en/latest/015_writing_modules.html) to make loading this particular `anaconda` installment easy and fun. + +Create a text file with the following contents. + +``` +help( +[[ anaconda(version 3-8.202105) Anaconda, containing CellProfiler 4.1.3 +]]) + +whatis("anaconda(version 3-8.202105) Anaconda, containing CellProfiler 4.1.3") + +local version = "3-8.202105" + +local base = "/hpc/local/$MY_DISTRO/$MY_GROUP/software/Anaconda3_2021_05" + +conflict("anaconda") + +prepend_path("PATH", pathJoin(base, "bin")) + +``` + +Your administrator can tell you precisely where these `modulefiles` should be stored. In our case, they are in a folder `/hpc/local/CentOS7/dhl_ec/etc/modulefiles/anaconda` for our group. Save the text-file as `3-8.2021.05.lua` in the `modulesfiles`-directory. + +Restart your shell: + +``` +source $HOME/.bashrc +source $HOME/.bash_profile +``` + +You can now load your fresh `anaconda` installation: + +``` +module load anaconda/3-8.202105 +``` + + +### Step 5: installation required packages + + +``` +conda install -c anaconda automake cmake enum34 requests libtiff libpng freetype zlib numpy scipy cython +``` + + +``` +conda install -c c4aarch64 autoconf +``` + + +``` +conda install -c conda-forge zbar openjpeg giflib libjpeg-turbo wmctrl parallel pylibdmtx imagemagick matplotlib gtk3 +``` + + +``` +conda install -c bioconda bftools java-jdk +``` + + +### Step 6: installation slideToolkit +Download and install the latest version of the slideToolkit from github. First create and go to the git directory, then download the slideToolkit. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/slideToolkit/.git ]; then \ + cd ~/git/slideToolkit && git pull; \ + else \ + cd ~/git/ && git clone https://github.com/swvanderlaan/slideToolKit.git; \ + fi +``` + +Add symbolic links in `~/bin/`. Now the slideToolkit will be availabe in your PATH. Adding the slideToolkit tools to your PATH makes it easier to acces the slideToolkit commands. + +``` +mkdir -p ~/bin/ && ln -s -f -v ~/git/slideToolkit/slide* ~/bin/ +``` + + +### Step 7: make a module for slideToolKit + +Let's create a `modulefile` to make loading `slideToolKit` easy and fun. + +Create a text file with the following contents. + +``` +help( +[[ slideToolKit(version 1.0) slideToolKit +]]) + +whatis("slideToolKit(version 1.0) slideToolKit") + +local version = "1.0" + +local base = "/hpc/local/$MY_DISTRO/$MY_GROUP/software/slideToolKit/" .. version + +conflict("slideToolkit") + +prepend_path("PATH", base) + +load("gnu-parallel/20170122") +prereq("gnu-parallel/20170122") + +load("zlib/1.2.8") +prereq("zlib/1.2.8") + +load("imagemagick/6.9.3-10") +prereq("imagemagick/6.9.3-10") + +load("openslide/3.4.1") +prereq("openslide/3.4.1") + +load("libdmtx/0.7.4") +prereq("libdmtx/0.7.4") + +load("dmtx-utils/0.7.4") +prereq("dmtx-utils/0.7.4") + +load("graphicsmagick/1.3.26") +prereq("graphicsmagick/1.3.26") + +``` + +Save the text-file as `/hpc/local/CentOS7/dhl_ec/etc/modulefiles/slidetoolkit/version1.0.lua` in the `modulesfiles`-directory. + +Restart your shell: + +``` +source $HOME/.bashrc +source $HOME/.bash_profile +``` + +You can now load your fresh `slideToolKit` installation: + +``` +module load slideToolKit/version1.0 +``` + + +### Step 8: installation CellProfiler + +Follow these instructions to install [CellProfiler](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-(Linux)). + +[instructions forthcoming] + + + +## Cleanup, restart & you're done! +Source your `bash_profile`. + +``` +source ~/.bash_profile +``` + +And make sure you load the new modules: + +``` +module load anaconda/3-8.202105 slideToolKit +``` + +------- +## To do + +- [x] add description on installation `anaconda`. +- [x] add description on how to create `anaconda` module. +- [x] add description on how to install required packages for `anaconda` and `slideToolKit`. +- [x] add description on how to create `slideToolKit` module. +- [.] add description on how to install `cellprofiler`. +- [x] update manual instructions . + + + + diff --git a/legacy/guides/INSTALL.OSX.anaconda.md b/legacy/guides/INSTALL.OSX.anaconda.md new file mode 100644 index 0000000..eee08fe --- /dev/null +++ b/legacy/guides/INSTALL.OSX.anaconda.md @@ -0,0 +1,222 @@ +macOS and OS X 10.9+ - slideToolkit installation instructions +============ + +The **slideToolKit** is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your current slideToolkit installation. + +We have tested **slideToolKit** on CentOS6, CentOS7, OS X Mountain Lion+ (version 10.8.[x]+), macOS Sierra (version 10.12.[x]), macOS High Sierra (version 10.13.[x]), macOS Mojave (version 10.14.[x]), and macOS Catalina (version 10.15.[x]). + +Please tell us if you run into problems, it is likely we can help you out - we have done this before. ;) + +-------------- + +#### Some installation basics + +We tried to create as few steps as possible with one-liners that are *easy* to read. Most of the installation is done using the commandline. You can copy/paste each example command, per block of code. For some steps you need administrator privileges. Follow the steps in consecutive order. + +``` +these `mono-type font` illustrate commands illustrate terminal commands. You can copy & paste these. +``` + +To make it easier to copy and paste, long commands that stretch over multiple lines are structered as follows: + +``` +Multiline commands end with a dash \ + indent 4 spaces, and continue on the next line. \ + Copy & paste these whole blocks of code. +``` + +Although we made it easy to just select, copy and paste and run these blocks of code, it is not a good practise to blindly copy and paste commands. Try to be aware about what you are doing. And never, never run `sudo` commands without a good reason to do so. + +-------------- + +#### Step 1 - Update and prepare. +The system must be up-to-date. Go to the Apple menu on the top left, click "Software Update...", and click the "Update all" button. If the system asks you if you want to turn on automatic updates, select 'Turn on'. Restart your system if needed. + +Binairies are executed from your local `bin` folder. By default this folder does not exists and is not present in your PATH. Create your `~/bin` and add it to your PATH if needed. + +``` +if ! [[ "$PATH" =~ (:$HOME/bin:|:~/bin:) ]] ; then \ + mkdir -p ~/bin && \ + printf "\n# Add ~/bin to your PATH\nexport PATH=\"~/bin:\$PATH\" \n" >> ~/.bash_profile + fi +``` + +Now we are up to date, and ready to continue the installation. + +#### Step 2 - Install XQuartz, a version of the X.Org X Window System that runs on OS X. +XQuartz is needed. Go to [xquartz.macosforge.org](http://xquartz.macosforge.org), download and install the latest stable version of XQuartz. You can find it under "Quick Download". + +On the end of the installation you are asked to log out and log back in, and of course you comply. + +#### Step 3 - Install brew đŸē, the missing package manager for OS X. +We install [brew](http://brew.sh) using the following one-liner. You need administrator rights for the installation. No characters will appear on the screen when you type in your password. + +``` +/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" +``` +P.S. You can check out more handy Homebrew repositories here: https://github.com/Homebrew. + +When asked if you want to install the "command line developer tools", choose 'Install'. After the "command line developer tools" installation, continue the installation in the Terminal + +After a `brew` installation, it is wise to do a `brew doctor` and follow the instructions on the screen. + +``` +brew doctor +``` +A final update and upgrade for your `brew` packages + +``` +brew update && brew upgrade +``` + +From now on, we asume your `brew` package manager is good to go. + +#### Step 4 - Install required libraries and packages using brew đŸē. +We install most packages using brew. + +``` +brew install automake wget jpeg libpng libtiff parallel openslide wmctrl zbar +``` + +#### Step 5 - Install `ImageMagick`. +First, we will uninstall *all* previous installations of `ImageMagick`, before we build it from source. + +``` +brew uninstall --ignore-dependencies --force imagemagick +``` + +Now, we are ready to install the latest `ImageMagick` from brew đŸē and from _source_, because otherwise `slideEMask` will not work. + +``` +brew install -s imagemagick +``` + +Note: We need ImageMagick 7+ (IM7) which is ported from ImageMagick 6 (IM6) and has an important change with respect to images. It has improved with respect to high dynamic range imaging (HDRI) by default. The default in IM7 is to update the RGBA channels. Previously in IMv6, the default was RGB. To handle this `slide2Tiles`, which uses `convert` from IM7 was edited to handle these channels: + +Old IM6 version: + +``` +convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" +``` + + +New IM7 version: + +``` +convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -channel RGB -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" +``` + +For reference check [this link](https://imagemagick.org/script/porting.php). + +#### Step 6 - Install the bioformat tools. +Install the latest version of BioFormats, including `bfconvert`. + +``` +mkdir -p ~/usr +``` +``` +cd ~/usr && wget http://downloads.openmicroscopy.org/latest/bio-formats5/artifacts/bftools.zip && \ + unzip -o bftools.zip && \ + rm bftools.zip +``` +Add symbolic links in `~/bin/`. Now the BioFormats tools will be availabe in your PATH. Adding the bftools to your PATH is obligatory for the **slideToolKit** to find its dependencies. + +``` +mkdir -p ~/bin/ && \ + ln -s -f -v ~/usr/bftools/bfconvert ~/bin/ && \ + ln -s -f -v ~/usr/bftools/domainlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/formatlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/ijview ~/bin/ && \ + ln -s -f -v ~/usr/bftools/mkfake ~/bin/ && \ + ln -s -f -v ~/usr/bftools/showinf ~/bin/ && \ + ln -s -f -v ~/usr/bftools/tiffcomment ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlindent ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlvalid ~/bin/ +``` + +#### Step 7 - Install datamatrix barcode libraries. +Install the latest version of `libdmtx`, including `dmtxread`. First we install the libraries: + +``` +brew install libdmtx +``` + +Luckily, `dmtx-utils` was updated to work with both `ImageMagick 6+` and `ImageMagick 7+`, and thus it was restored from `homebrew/boneyard`. See also: https://github.com/Homebrew/homebrew-core/pull/10693 and https://github.com/dmtx/dmtx-utils/issues/2. Now we can install it the easy way, using brew đŸē. + +``` +brew install dmtx-utils +``` + +The dmtx and libdmtx binairies are installed in `/usr/local/bin`. This is the folder `brew` uses for its installations and should already be in your PATH. + +#### Step 8 - Install slideToolkit. +Download and install the latest version of the **slideNormalize** from GitHub. First create and go to the git directory, then download the **slideToolKit**. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/slideToolKit/.git ]; then \ + cd ~/git/slideToolKit && git pull; \ + else \ + cd ~/git/ && git clone https://github.com/swvanderlaan/slideToolKit.git; \ + fi +``` + +Add symbolic links in `~/bin/`. Now the **slideToolKit** will be availabe in your PATH. Adding the **slideToolKit** tools to your PATH makes it easier to acces the slideToolkit commands. + +``` +mkdir -p ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slide2Tiles ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideConvert ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideDirectory ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideEMask ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideInfo ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideJobsCellProfiler ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMask ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMacro ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideQuantify ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideQuantifyLocal ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideRename ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideReset ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideSQLheader ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideThumb ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideExtract.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideInfo.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMacro.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideThumb.py ~/bin/ + +``` + +#### Step 9 - Instal NDPITools + +Some pathology scanners produce proprietary `NDPI`-files. These are almost similar to `TIF`, but with some differences. To be able to run _slideToolKit_ and _CellProfiler_ we need to install a small package `NDPITools` to convert `NDPI`-files to `TIF`. + +Simply go to the [NDPITools website](https://www.imnc.in2p3.fr/pagesperso/deroulers/software/ndpitools/) and following the instructions for macOS. + +#### Step 10 - Install CellProfiler, we prefer version 2.2.0. +Install `CellProfiler version 2.2.0` following instructions on their [website](http://cellprofiler.org/download.shtml). Using the downloaded installer, CellProfiler will be installed in the default location (/Applications/CellProfiler). + +To make the CellProfiler command line interface (CLI) available, we create a `cellprofiler` script in your `~/bin` folder. This scripts links to CellProfiler installed in your /Applications folder. + +``` +printf '#!/bin/bash\n# run cellprofiler from CLI\n/Applications/CellProfiler.app/Contents/MacOS/CellProfiler "$@"\n' \ + > ~/bin/cellprofiler && chmod -v 0755 ~/bin/cellprofiler + +``` + +##### Alternative versions of CellProfiler +We prefer version 2.2.0 because this version works best on our _high-performance computing cluster_. That said: one could easily install the latest version by simply changing the version number of CellProfiler in the command above, if needed. For example: + +``` +printf '#!/bin/bash\n# run cellprofiler from CLI\n/Applications/CellProfiler-3.1.8.app/Contents/MacOS/cp "$@"\n' \ + > ~/bin/cellprofiler && chmod -v 0755 ~/bin/cellprofiler +``` + +_Note: an alternative installation instruction for CellProfiler could be found [here](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-%28OS-X-and-macOS%29)_ + + +> Disclaimer: we have not fully tested our workflow and slideToolKit with newer versions of CellProfiler. Presumably `pipelines` need to be edited to fit the new version. + +#### Step 11 - Reboot. +Reboot your system and you're done. diff --git a/legacy/guides/INSTALL.OSX.md b/legacy/guides/INSTALL.OSX.md new file mode 100755 index 0000000..eee08fe --- /dev/null +++ b/legacy/guides/INSTALL.OSX.md @@ -0,0 +1,222 @@ +macOS and OS X 10.9+ - slideToolkit installation instructions +============ + +The **slideToolKit** is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your current slideToolkit installation. + +We have tested **slideToolKit** on CentOS6, CentOS7, OS X Mountain Lion+ (version 10.8.[x]+), macOS Sierra (version 10.12.[x]), macOS High Sierra (version 10.13.[x]), macOS Mojave (version 10.14.[x]), and macOS Catalina (version 10.15.[x]). + +Please tell us if you run into problems, it is likely we can help you out - we have done this before. ;) + +-------------- + +#### Some installation basics + +We tried to create as few steps as possible with one-liners that are *easy* to read. Most of the installation is done using the commandline. You can copy/paste each example command, per block of code. For some steps you need administrator privileges. Follow the steps in consecutive order. + +``` +these `mono-type font` illustrate commands illustrate terminal commands. You can copy & paste these. +``` + +To make it easier to copy and paste, long commands that stretch over multiple lines are structered as follows: + +``` +Multiline commands end with a dash \ + indent 4 spaces, and continue on the next line. \ + Copy & paste these whole blocks of code. +``` + +Although we made it easy to just select, copy and paste and run these blocks of code, it is not a good practise to blindly copy and paste commands. Try to be aware about what you are doing. And never, never run `sudo` commands without a good reason to do so. + +-------------- + +#### Step 1 - Update and prepare. +The system must be up-to-date. Go to the Apple menu on the top left, click "Software Update...", and click the "Update all" button. If the system asks you if you want to turn on automatic updates, select 'Turn on'. Restart your system if needed. + +Binairies are executed from your local `bin` folder. By default this folder does not exists and is not present in your PATH. Create your `~/bin` and add it to your PATH if needed. + +``` +if ! [[ "$PATH" =~ (:$HOME/bin:|:~/bin:) ]] ; then \ + mkdir -p ~/bin && \ + printf "\n# Add ~/bin to your PATH\nexport PATH=\"~/bin:\$PATH\" \n" >> ~/.bash_profile + fi +``` + +Now we are up to date, and ready to continue the installation. + +#### Step 2 - Install XQuartz, a version of the X.Org X Window System that runs on OS X. +XQuartz is needed. Go to [xquartz.macosforge.org](http://xquartz.macosforge.org), download and install the latest stable version of XQuartz. You can find it under "Quick Download". + +On the end of the installation you are asked to log out and log back in, and of course you comply. + +#### Step 3 - Install brew đŸē, the missing package manager for OS X. +We install [brew](http://brew.sh) using the following one-liner. You need administrator rights for the installation. No characters will appear on the screen when you type in your password. + +``` +/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" +``` +P.S. You can check out more handy Homebrew repositories here: https://github.com/Homebrew. + +When asked if you want to install the "command line developer tools", choose 'Install'. After the "command line developer tools" installation, continue the installation in the Terminal + +After a `brew` installation, it is wise to do a `brew doctor` and follow the instructions on the screen. + +``` +brew doctor +``` +A final update and upgrade for your `brew` packages + +``` +brew update && brew upgrade +``` + +From now on, we asume your `brew` package manager is good to go. + +#### Step 4 - Install required libraries and packages using brew đŸē. +We install most packages using brew. + +``` +brew install automake wget jpeg libpng libtiff parallel openslide wmctrl zbar +``` + +#### Step 5 - Install `ImageMagick`. +First, we will uninstall *all* previous installations of `ImageMagick`, before we build it from source. + +``` +brew uninstall --ignore-dependencies --force imagemagick +``` + +Now, we are ready to install the latest `ImageMagick` from brew đŸē and from _source_, because otherwise `slideEMask` will not work. + +``` +brew install -s imagemagick +``` + +Note: We need ImageMagick 7+ (IM7) which is ported from ImageMagick 6 (IM6) and has an important change with respect to images. It has improved with respect to high dynamic range imaging (HDRI) by default. The default in IM7 is to update the RGBA channels. Previously in IMv6, the default was RGB. To handle this `slide2Tiles`, which uses `convert` from IM7 was edited to handle these channels: + +Old IM6 version: + +``` +convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" +``` + + +New IM7 version: + +``` +convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -channel RGB -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" +``` + +For reference check [this link](https://imagemagick.org/script/porting.php). + +#### Step 6 - Install the bioformat tools. +Install the latest version of BioFormats, including `bfconvert`. + +``` +mkdir -p ~/usr +``` +``` +cd ~/usr && wget http://downloads.openmicroscopy.org/latest/bio-formats5/artifacts/bftools.zip && \ + unzip -o bftools.zip && \ + rm bftools.zip +``` +Add symbolic links in `~/bin/`. Now the BioFormats tools will be availabe in your PATH. Adding the bftools to your PATH is obligatory for the **slideToolKit** to find its dependencies. + +``` +mkdir -p ~/bin/ && \ + ln -s -f -v ~/usr/bftools/bfconvert ~/bin/ && \ + ln -s -f -v ~/usr/bftools/domainlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/formatlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/ijview ~/bin/ && \ + ln -s -f -v ~/usr/bftools/mkfake ~/bin/ && \ + ln -s -f -v ~/usr/bftools/showinf ~/bin/ && \ + ln -s -f -v ~/usr/bftools/tiffcomment ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlindent ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlvalid ~/bin/ +``` + +#### Step 7 - Install datamatrix barcode libraries. +Install the latest version of `libdmtx`, including `dmtxread`. First we install the libraries: + +``` +brew install libdmtx +``` + +Luckily, `dmtx-utils` was updated to work with both `ImageMagick 6+` and `ImageMagick 7+`, and thus it was restored from `homebrew/boneyard`. See also: https://github.com/Homebrew/homebrew-core/pull/10693 and https://github.com/dmtx/dmtx-utils/issues/2. Now we can install it the easy way, using brew đŸē. + +``` +brew install dmtx-utils +``` + +The dmtx and libdmtx binairies are installed in `/usr/local/bin`. This is the folder `brew` uses for its installations and should already be in your PATH. + +#### Step 8 - Install slideToolkit. +Download and install the latest version of the **slideNormalize** from GitHub. First create and go to the git directory, then download the **slideToolKit**. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/slideToolKit/.git ]; then \ + cd ~/git/slideToolKit && git pull; \ + else \ + cd ~/git/ && git clone https://github.com/swvanderlaan/slideToolKit.git; \ + fi +``` + +Add symbolic links in `~/bin/`. Now the **slideToolKit** will be availabe in your PATH. Adding the **slideToolKit** tools to your PATH makes it easier to acces the slideToolkit commands. + +``` +mkdir -p ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slide2Tiles ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideConvert ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideDirectory ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideEMask ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideInfo ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideJobsCellProfiler ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMask ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMacro ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideQuantify ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideQuantifyLocal ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideRename ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideReset ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideSQLheader ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideThumb ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideExtract.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideInfo.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideMacro.py ~/bin/ && \ + ln -s -f -v ~/git/slideToolKit/slideThumb.py ~/bin/ + +``` + +#### Step 9 - Instal NDPITools + +Some pathology scanners produce proprietary `NDPI`-files. These are almost similar to `TIF`, but with some differences. To be able to run _slideToolKit_ and _CellProfiler_ we need to install a small package `NDPITools` to convert `NDPI`-files to `TIF`. + +Simply go to the [NDPITools website](https://www.imnc.in2p3.fr/pagesperso/deroulers/software/ndpitools/) and following the instructions for macOS. + +#### Step 10 - Install CellProfiler, we prefer version 2.2.0. +Install `CellProfiler version 2.2.0` following instructions on their [website](http://cellprofiler.org/download.shtml). Using the downloaded installer, CellProfiler will be installed in the default location (/Applications/CellProfiler). + +To make the CellProfiler command line interface (CLI) available, we create a `cellprofiler` script in your `~/bin` folder. This scripts links to CellProfiler installed in your /Applications folder. + +``` +printf '#!/bin/bash\n# run cellprofiler from CLI\n/Applications/CellProfiler.app/Contents/MacOS/CellProfiler "$@"\n' \ + > ~/bin/cellprofiler && chmod -v 0755 ~/bin/cellprofiler + +``` + +##### Alternative versions of CellProfiler +We prefer version 2.2.0 because this version works best on our _high-performance computing cluster_. That said: one could easily install the latest version by simply changing the version number of CellProfiler in the command above, if needed. For example: + +``` +printf '#!/bin/bash\n# run cellprofiler from CLI\n/Applications/CellProfiler-3.1.8.app/Contents/MacOS/cp "$@"\n' \ + > ~/bin/cellprofiler && chmod -v 0755 ~/bin/cellprofiler +``` + +_Note: an alternative installation instruction for CellProfiler could be found [here](https://github.com/CellProfiler/CellProfiler/wiki/Source-installation-%28OS-X-and-macOS%29)_ + + +> Disclaimer: we have not fully tested our workflow and slideToolKit with newer versions of CellProfiler. Presumably `pipelines` need to be edited to fit the new version. + +#### Step 11 - Reboot. +Reboot your system and you're done. diff --git a/guides/INSTALL.Ubuntu 16.04 LTS CellProfiler.sh b/legacy/guides/INSTALL.Ubuntu 16.04 LTS CellProfiler.sh similarity index 100% rename from guides/INSTALL.Ubuntu 16.04 LTS CellProfiler.sh rename to legacy/guides/INSTALL.Ubuntu 16.04 LTS CellProfiler.sh diff --git a/guides/INSTALL.Ubuntu 16.04 LTS slideToolkit.sh b/legacy/guides/INSTALL.Ubuntu 16.04 LTS slideToolkit.sh similarity index 100% rename from guides/INSTALL.Ubuntu 16.04 LTS slideToolkit.sh rename to legacy/guides/INSTALL.Ubuntu 16.04 LTS slideToolkit.sh diff --git a/legacy/guides/INSTALL.Ubuntu.12.04.LTS-PrecisePangolin.md b/legacy/guides/INSTALL.Ubuntu.12.04.LTS-PrecisePangolin.md new file mode 100755 index 0000000..b895006 --- /dev/null +++ b/legacy/guides/INSTALL.Ubuntu.12.04.LTS-PrecisePangolin.md @@ -0,0 +1,256 @@ +Ubuntu 12.04 - slideToolkit installation instructions +============ + +The slideToolkit is a set of scripts that requires other programs and libraries to run. Here we explain the dependencies and show instructions on how to install these dependencies. The required dependencies can change and might break your curren slideToolkit installation. + +Please tell us if you run into problems, it is likely we can help you out, we have done this before ;) + +I tried to create as few steps as possible with one-liners that are *easy* to read. Most of the installation is done using the commandline. You can copy/paste each example command, per block of code. For some steps you need administrator privileges. Follow the steps in consecutive order. + +``` +these `mono-type font` illustrate commands illustrate terminal commands. You can copy & paste these. +``` + +To make it easier to copy and paste, long commands that stretch over multiple lines are structed as follows: + +``` +Multiline commands end with a dash \ + indent 4 spaces, and continue on the next line. \ + Copy & paste these whole blocks of code. +``` + +Although we made it easy to just select, copy and paste and run these blocks of code, it is not a good practise to blindly copy and paste commands. Try to be aware about what you are doing. And never, never run `sudo` commands without a good reason to do so. + +------------ + +#### Step 1 - Update and prepare +The system must be up-to-date. Install updates, answer --yes to everything. Make sure you stay on version 12.04 and do not upgrade to Ubuntu 14 (Trusty Tar). This can take a while. + +``` +sudo apt-get --yes update && sudo apt-get --yes upgrade && \ + sudo apt-get --yes dist-upgrade && sudo apt-get --yes autoremove +``` + +Binairies are executed from your local `bin` folder. Create your `~/bin` and add it to your PATH if needed. + +``` +if ! [[ "$PATH" =~ (:$HOME/bin:|:~/bin:) ]] ; then \ + mkdir -p ~/bin && \ + printf "\n# Add ~/bin to your PATH\nexport PATH=\"~/bin:\$PATH\" \n" >> ~/.profile + fi +``` + +On Ubuntu Linux you need to reboot for the PATH to be found. + +``` +sudo reboot +``` + +Now we are up to date, and ready to continue the installation. + +#### Step 2 - Install required libraries and packages using apt-get +This apt-get oneliner will install most of the important packages we need and takes take of most dependencies as well. + +``` +sudo apt-get --yes update && sudo apt-get --yes install autoconf \ + automake "build-essential" curl cvs gimp git "libgtk2.0-dev" \ + "libjpeg-dev" "libopenjpeg-dev" "libopenslide-dev" "libsqlite3-dev" \ + libtool "libxml2-dev" parallel perl "pkg-config" vim wget wmctrl \ + "zbar-tools" +``` + +Most dependcies are now installed, but we need some more. + +#### Step 3 - Install zlib +Install the latest zlib compression libraries. First create and go to the src directory, then download and extract zlib. + +``` +mkdir -p ~/src && cd ~/src && \\ + wget http://zlib.net/zlib-1.2.8.tar.gz -O zlib-1.2.8.tar.gz && \ + tar xzvf zlib-1.2.8.tar.gz && \ + rm zlib-1.2.8.tar.gz +``` +Install zlib. + +``` +cd ~/src/zlib-1.2.8 +``` +``` +./configure && make && sudo make install && make clean +``` + +#### Step 4 - Install libtiff +Install the latest libtiff library using cvs. When `cvs` asks for a password for the anonymous login, just press enter. The funny thing is, `apt-get install libtiff4` does install libtiff 3.9.* . But we need libtiff 4.* for BigTIFF support. First create and go to the cvs directory, then download and extract libtiff. + +``` +mkdir -p ~/cvs && cd ~/cvs +``` +``` +cvs -d :pserver:cvsanon:@cvs.maptools.org:/cvs/maptools/cvsroot checkout libtiff +``` +Install libtiff. + +``` +cd ~/cvs/libtiff +``` +``` +./configure && make && sudo make install && make clean +``` + +#### Step 5 - Install ImageMagick +Download and install the latest version ImageMagick from there website. First create and go to the src directory, then download and extract ImageMagick. + +``` +mkdir -p ~/src/ && cd ~/src +``` +``` +wget http://www.imagemagick.org/download/ImageMagick.tar.gz -O ImageMagick.tar.gz && \ + tar xzfv ImageMagick.tar.gz && \ + rm ImageMagick.tar.gz +``` + +Install ImageMagick. + +``` +cd ~/src/ImageMagick* +``` +``` +./configure && make && sudo make install && make clean +``` + +After the ImageMagick installation we need to examine the libraries, update links and cache where necessary. Else ImageMagick would work properly. + +``` +sudo ldconfig /usr/local/lib +``` + +#### Step 6 - Install openslide +Download the latest version of openslide from github. Pull if already exists; clone if none existing. First create and go to the git directory, then download the source. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/openslide/.git ]; then \ + cd ~/git/openslide && git pull; \ + else \ + cd ~/git/ && git clone git://github.com/openslide/openslide.git; \ + fi +``` + +Install openslide. + +``` +cd ~/git/openslide +``` +``` +autoreconf -i +``` +``` +./configure && make && sudo make install && make clean +``` + +#### Step 7 - Install bfconvert +Download and install the latest version of bfconvert. First create and go to the usr directory, then download and extract bftools. + +``` +mkdir -p ~/usr && cd ~/usr +``` +``` +wget http://downloads.openmicroscopy.org/latest/bio-formats5/artifacts/bftools.zip && \ + unzip -o bftools.zip && \ + rm bftools.zip +``` +No need to configure the bftools. We only need to add symbolic links in `~/bin`, this will make the BioFormats availabe within your PATH. Adding the bftools to your PATH is obligatory for the slideToolkit to find its dependencies. + +``` +mkdir -p ~/bin/ && ln -s -f -v ~/usr/bftools/bfconvert ~/bin/ && \ + ln -s -f -v ~/usr/bftools/domainlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/formatlist ~/bin/ && \ + ln -s -f -v ~/usr/bftools/ijview ~/bin/ && \ + ln -s -f -v ~/usr/bftools/mkfake ~/bin/ && \ + ln -s -f -v ~/usr/bftools/showinf ~/bin/ && \ + ln -s -f -v ~/usr/bftools/tiffcomment ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlindent ~/bin/ && \ + ln -s -f -v ~/usr/bftools/xmlvalid ~/bin/ +``` + +#### Step 8 - Install datamatrix barcode libraries +Download and install the latest version of the datamatrix barcode libraries and binairies (`dmtx`) from sourceforge using git. First create and go to the git directory, then download and extract the libraries. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/libdmtx/.git ]; then \ + cd ~/git/libdmtx && git pull; \ + else \ + cd ~/git/ && git clone git://libdmtx.git.sourceforge.net/gitroot/libdmtx/libdmtx; \ + fi +``` +Install the datamatrix barcode libraries + +``` +cd ~/git/libdmtx && mkdir -p m4 && autoreconf --force --install +``` +``` +./configure && make && sudo make install && make clean +``` +Now the binairies. First create and go to the git directory, then download and extract the binairies. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/dmtx-utils/.git ]; then \ + cd ~/git/dmtx-utils && git pull; \ + else \ + cd ~/git/ && git clone git://git.code.sf.net/p/libdmtx/dmtx-utils; \ + fi +``` + +Install the datamatrix barcode binairies. + +``` +cd ~/git/dmtx-utils && mkdir -p m4 && autoreconf --force --install +``` +``` +./configure && make && sudo make install && make clean +``` +#### Step 9 - Install slideToolkit +Download and install the latest version of the slideToolkit from github. First create and go to the git directory, then download the slideToolkit. + +``` +mkdir -p ~/git/ && cd ~/git +``` +``` +if [ -d ~/git/slideToolkit/.git ]; then \ + cd ~/git/slideToolkit && git pull; \ + else \ + cd ~/git/ && git clone https://github.com/bglnelissen/slideToolkit.git; \ + fi +``` + +Add symbolic links in `~/bin/`. Now the slideToolkit will be availabe in your PATH. Adding the slideToolkit tools to your PATH makes it easier to acces the slideToolkit commands. + +``` +mkdir -p ~/bin/ && ln -s -f -v ~/git/slideToolkit/slide* ~/bin/ +``` + + +#### Step 10 - Install CellProfiler +We have no step-by-step installation instructions for CellProfiler yet. But you can follow the instructions on [cellprofiler.org](http://www.cellprofiler.org), or on the CellProfiler [github wiki page](https://github.com/CellProfiler/CellProfiler/wiki/CellProfiler-Developer%27s-version-installation-for-Linux). + +You can also try the 'Docker' version of CellProfiler, which is known to work with Ubuntu and can be found [here](https://github.com/hajaalin/docker-cellprofiler). + +#### Step 11 - Cleanup, restart & you're done! +Fix linked libraries. + +``` +sudo ldconfig +``` +Restart. + +``` +sudo reboot +``` diff --git a/guides/Readme.md b/legacy/guides/Readme.md similarity index 91% rename from guides/Readme.md rename to legacy/guides/Readme.md index a2a4cd2..29a4347 100755 --- a/guides/Readme.md +++ b/legacy/guides/Readme.md @@ -60,11 +60,13 @@ In digital image manipulation, a mask defines what part of the image will be ana The tools designed for step 2: - `slideDirectory`, create a staging directory per slide. -- `slideThumb`, create slide thumbnail, including label. -- `slideMask`, create a scaled mask and macro version from a slide. +- `slideThumb`, create slide thumbnail, including label (in *PNG* format). +- `slideMacro`, create a scaled macro version from a slide (in *PNG* format). +- `slideEntropyMasker`, create image masks form PNG-files. -Sometimes `slideMask` will be unable to make proper masks, especially when the contrast between tissue and background is very low. One can than divert to `slideMaskEntropy.py` which takes a PNG-file and makes a mask off of it. +Note: As sometimes the former `slideMask` was unable to make proper masks, especially when the contrast between tissue and background is very low, we created `slideEntropyMasker`. +###### UPDATE THIS SECTION ##### ###### Installation instructions for `slideMaskEntropy.py`: `pip install numpy matplotlib scikit-image` @@ -73,6 +75,9 @@ We assume that `slideMask` has been run, but all the *.mask.png are of bad quali `for FILE in $(pwd)/*.macro.PNG ; do echo "* Processing [ "$FILE" ]..."; slideMaskEntropy.py "$FILE"; done` +###### ----------- ##### + + ##### Step 3 - tiles Image analysis of memory intensive, whole 20x representations of the digitized slides is currently impossible due to hardware and software limitations. The goal of this step is to create multiple smaller images (i.e. tiles) from the 20x whole slide image. An upscaled version of the mask is placed over the 20x whole slide image (in our example this is layer 3 of the multi layered TIFF). Image manipulation on 20x sized whole slide images requires large amounts of computer RAM. To make it possible for computers without sufficient RAM to handle these files, the slideToolkit uses a memory-mapped disk file of the program memory. Using disk mapped memory files (ImageMagick .mpc files), the slideToolkit can efficiently extract all tiles. Without a mask, a faster and more memory efficient method is used using the openslide library. @@ -89,8 +94,8 @@ CellProfiler is able to output its measurements in SQL/CSV format. Were the SQL The tools designed for step 4: - - `slideJobsCellProfiler`, outputs a list of jobs for CellProfiler - - `slideSQLheader`, fetch table headers from CellProfiler SQL output for CSV file + - `slideJobsCellProfiler`, outputs a list of jobs for CellProfiler. + - `slideSQLheader`, fetch table headers from CellProfiler SQL output for CSV file. --- @@ -118,20 +123,17 @@ The programs and libraries you need to run the slideToolkit: - [Openslide](http://openslide.org) >= version 3.4 - [Perl](http://www.perl.org) >= version 5 - [slideToolkit](https://github.com/swvanderlaan/slideToolkit) +- [NDPItools](https://www.imnc.in2p3.fr/pagesperso/deroulers/software/ndpitools/), click the link at 'Mac OS 10.6 and later' on the right side; follow the installation instructions. *The latest stable version of these programs and libraries should be sufficient. There is one catch, the Lib TIFF library supports the TIFF64 (aka BigTIFF) format since version 4.* ### Installation instructions: -Installation instructions for OS X and Linux can be found in this repo. +Installation instructions for OS X and Linux can be found in this repo. *We are working on updates!* -<<<<<<< HEAD -- [INSTALL - OS X 10.[8/9/10/11.[x]] - Mountain Lion/Mavericks/Yosemite/El Capitan](INSTALL.OSX.md) *(Completely updated - mostly applicable to Ubuntu or CentOS6.6+ too)* +- [INSTALL - OS X 10.[8/9/10/11.[x]] - Mountain Lion/Mavericks/Yosemite/El Capitan/Sierra/High Sierra](INSTALL.OSX.md) *(Largely updated - mostly applicable to Ubuntu or CentOS6.6+ too)* - [INSTALL - Ubuntu 12.04 LTS - Precise Pangolin](INSTALL.Ubuntu.12.04.LTS-PrecisePangolin.md) *(CellProfiler instructions incomplete)* -- [INSTALL - CentOS 7](INSTALL.CentOS7.md) *(In progress)* +- [INSTALL - CentOS 7](INSTALL.CentOS7.admin.md) *(In progress)* - *We have not planned to create installation instructions for Microsoft Windows. Try to run CentOS 6.6+ within [VirtualBox](https://www.virtualbox.org) instead.* -*Finally a 'thank you' for the developers of ImageMagick and CellProfiler, for there support and excelent software.* -======= -*Finally a 'thank you' for the teams of ImageMagick, CellProfiler and all the other great software packeges for there support and excelent software* ->>>>>>> bglnelissen/master +*Finally a 'thank you' for the teams of ImageMagick, CellProfiler and all the other great software packages for there support and excellent software.* diff --git a/legacy/guides/slideToolkit.workflow.tif b/legacy/guides/slideToolkit.workflow.tif new file mode 100755 index 0000000..103a85c Binary files /dev/null and b/legacy/guides/slideToolkit.workflow.tif differ diff --git a/legacy/slideQuantifyDev b/legacy/slideQuantifyDev new file mode 100755 index 0000000..74bb21c --- /dev/null +++ b/legacy/slideQuantifyDev @@ -0,0 +1,352 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #3 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #4 -- starting letters/characters of the image-subdirectories, e.g. IMG or AE or AAA." + echoerror "- Argument #5 -- output filename where the CellProfiler results are stored, e.g. Image.txt (delimiter is assumed '_')." + echoerror "- Argument #6 -- email address to send aborted/error jobs too, e.g. y.name@umcutrecht.nl." + echoerror "- Argument #7 -- by default emails are send when jobs are aborted/error, you can change that here, e.g. you can choose: b=begin of job; e=end of job; a=abort of job; s=suspended job; n=no mail is send." + echoerror "- Argument #8 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify [arg1: STAIN] [arg2: path_to_cellprofiler_pipeline] [arg3: path_to_working_directory] [arg4: IMG or AE or AAA] [arg5: Image.txt] [arg6: y.name@umcutrecht.nl] [arg7: a] [arg8: 210]" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify -- Development" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-07-23" +echoitalic "* Version : 1.1.8" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory. Use for development, no" +echoitalic " intermediate files are removed." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +PIPELINE="$2" # Depends on arg2 +PROJECTDIR="$3" # Depends on arg3 +FILESTART="$4" # Depends on arg4 +OUTPUTFILENAME="$5" # Depends on arg5 +EMAIL="$6" # Depends on arg6 +MAILSETTING="$7" # Depends on arg7 +EMASKTHRESHOLD="$8" # Depends on arg8 + +QJOB_MEM_SLIDEMASK="100G" +QJOB_TIME_SLIDEMASK="02:30:00" + +QJOB_MEM_SLIDE2TILES="150G" +QJOB_TIME_SLIDE2TILES="04:30:00" + +QJOB_MEM_SLIDENORM="5G" +QJOB_TIME_SLIDENORM="02:00:00" + +QJOB_MEM_CP="10G" +QJOB_TIME_CP="09:00:00" + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 8 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify ***!" + +else + echobold "Cleaning up house, first." + mkdir -pv "$PROJECTDIR"/logs + mkdir -pv "$PROJECTDIR"/errors + rm -rfv "$FILESTART"*/magick-tmp + + ENTROPY_THRESHOLD="$EMASKTHRESHOLD" # should be argument + HOLD_JIDS=() + INDEX=0 + BATCH_SIZE=5000 # should be argument, but how exactly does this work? + UNIQUE_LABEL="V3" # should be argument + + ### FOR DEBUG + ### DEBUGLIST="AE999.FIBRIN AE99.T02-18575.A.FIBRIN AE997.T05-12460.FIBRIN" + ### for SLIDE_NUM in $DEBUGLIST; do + for SLIDE_NUM in "$FILESTART"*; do + sleep 1 + THIS_SLIDE=${SLIDE_NUM/\//} + THIS_SLIDEMASK=${THIS_SLIDE}_slideMask_${UNIQUE_LABEL} + THIS_SLIDE2TILES=${THIS_SLIDE}_slide2Tiles_${UNIQUE_LABEL} + THIS_SLIDENORMALIZE=${THIS_SLIDE}_slideNormalize_${UNIQUE_LABEL} + THIS_CELLPROFILER=${THIS_SLIDE}_CellProfiler_${UNIQUE_LABEL} + THIS_WRAPUP=${THIS_SLIDE}_WrapUp_${UNIQUE_LABEL} + + HOLD_JID_INDEX=$(expr ${INDEX} % ${BATCH_SIZE}) + + # process batches in smaller chunks + if [ "$INDEX" -lt "$BATCH_SIZE" ]; then + HOLD_JID_STRING="" + else + HOLD_JID_STRING="-hold_jid ${HOLD_JIDS[$HOLD_JID_INDEX]}" + fi + HOLD_JIDS[$HOLD_JID_INDEX]=$THIS_CELLPROFILER + + cd "$SLIDE_NUM" || exit + echo "" + echobold "Processing images of file [ ${SLIDE_NUM} ] for staining [ ${STAIN} ] -- at index [ ${INDEX} ] with total batch size [ ${BATCH_SIZE} ]." + + if [[ -s cp_output/"$STAIN"_Image.csv.gz ]]; then + echoitalic "Checking if this file was processed before; cp_output/${STAIN}_Image.csv.gz should be present ....." + ls -lh cp_output/*.gz + cd .. ; + INDEX=$((INDEX+1)); + echo "..... Analysis was already done!"; + continue; + fi + + echo "..... Processing file for the first time. Submitting job for: " + + rm -rfv "$PROJECTDIR"/logs/"$SLIDE_NUM"* + rm -rfv "$PROJECTDIR"/errors/"$SLIDE_NUM"* + + ### slideEMask for macro, and NDPISplit when necessary + echo "" + echoitalic "* slideEMask, which will mask, create a macro, and do NDPISplit when necessary." + + echo "#if [ -s *mask.png ]; then exit; fi; + module load slideToolkit + module load ndpitools + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + if [ -f *.ndpi ]; then + echo \"The image-file is a NDPI and will be converted to .tif before masking.\" + if [ -f *.ndpi ]; then ndpisplit -x40 -z0 *.ndpi; fi + slideMask --layer 0 -f *.tif; + + elif [ -f *.tif ]; then + echo \"The image-file was NDPI and converted to .tif.\" + slideMask --layer 0 -f *.tif; + + elif [ -f *.TIF ]; then + echo \"The image-file is a .TIF.\" + slideMask --layer 3 -f *.TIF; + + else + echoerrorflash \"*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF' ]\" + exit 1 + fi + slideEMask -f *macro.png -t $ENTROPY_THRESHOLD + rm -rfv magick-tmp + " | qsub -cwd -N ${THIS_SLIDEMASK} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ ${HOLD_JID_STRING} -l h_vmem=${QJOB_MEM_SLIDEMASK} -l h_rt=${QJOB_TIME_SLIDEMASK} -m ${MAILSETTING} -M ${EMAIL} + ### FOR DEBUG + ### echo $THIS_SLIDEMASK + ### echo $HOLD_JID_STRING + + ### slide2Tiles + echo "" + echoitalic "* slide2Tiles to create tiles from images." + + echo "if [ -d *.tiles ]; then exit; fi; + module load ndpitools; + module load slideToolkit; + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + if [ -f *.ndpi ]; then + echo \"The image-file is a NDPI and will be converted to .tif before tiling.\" + slide2Tiles --layer 0 -f *x40*.tif -m *emask.png; + + elif [ -f *.tif ]; then + echo \"The image-file was NDPI and converted to .tif.\" + slide2Tiles --layer 0 -f *.tif -m *.emask.png; + + elif [ -f *.TIF ]; then + echo \"The image-file is a .TIF.\" + slide2Tiles --layer 3 -f *.TIF -m *.emask.png; + + else + echoerrorflash \"*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF' ]\" + exit 1 + fi + rm -rfv magick-tmp + " | qsub -cwd -N ${THIS_SLIDE2TILES} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDEMASK} -l h_vmem=${QJOB_MEM_SLIDE2TILES} -l h_rt=${QJOB_TIME_SLIDE2TILES} -m ${MAILSETTING} -M ${EMAIL} + + ### slideNormalize + echo "" + echoitalic "* slideNormalize for normalization of images." + + echo "if [ -s files2cp.txt ]; then exit; fi; + if [ ! -d *.tiles ]; then + (>&2 echo "*** ERROR *** No tiles to process. ") + exit; + fi + cd *.tiles/; + module load slideNormalize; + module load slideToolkit; + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + for f in *.png; do + echo \"...Processing tile \$f\"; + slideNormalize \$f; + slideEMask -c -f \$f -t $ENTROPY_THRESHOLD + rm -v \$f; + done + rm -rfv magick-tmp + cd .. + ls -d -1 \$PWD/*tiles/*normalized* > files2cp.txt; + ls -d -1 \$PWD/*tiles/ENTROPY* >> files2cp.txt; + " | qsub -cwd -N ${THIS_SLIDENORMALIZE} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDE2TILES} -l h_vmem=${QJOB_MEM_SLIDENORM} -l h_rt=${QJOB_TIME_SLIDENORM} -m ${MAILSETTING} -M ${EMAIL} + + ### CellProfiler + echo "" + echoitalic "* CellProfiler to analyze images." + + echo "if [ -d cp_output ]; then exit; fi; + module load anaconda + mkdir -pv cp_output + cellprofiler -c -r -p $PIPELINE --file-list files2cp.txt -o cp_output/; + " | qsub -cwd -N ${THIS_CELLPROFILER} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDENORMALIZE} -l h_vmem=${QJOB_MEM_CP} -l h_rt=${QJOB_TIME_CP} -m ${MAILSETTING} -M ${EMAIL} + + ### REMOVE THIS BREAK FOR PRODUCTION! + ### When this break is active, only the first item + ### in the list of items to be processed will be processed. +# INDEX=$((INDEX+1)) +# break; + + ### Wrap Up + echo "" + echoitalic "* WrapUp the work we have done, and clean house." + + # Obsolete code + # 1. we randomly grap 10 masked & tiled PNG-files to keep for post-analysis check >> OUT-COMMENTED + # 2. we get the data (in STAIN_Image.txt) for each study number (SLIDE_NUM) in 'cp_output' using >> OUT-COMMENTED + # Colsums.R + # 3. we remove tiles, and other files we don't need + + echo " + #ls cp_output/*.png | shuf -n \$(expr \$(ls cp_output/*.png | wc -l) - 10) | xargs rm; + echo 'SLIDE_NUM $STAIN' > results.txt; + cd cp_output; + SLIDE_NUM=\$(basename \"$PWD\") | cut -d'.' -f1 + echo $SLIDE_NUM \$(Rscript $SLIDETOOLKITDIR/utilities/Colsums.R $STAIN $OUTPUTFILENAME) >> ../results.txt; + cd .. + #rm -rfv *tiles/; + if [ -f *.ndpi ]; then + echo \"Removing intermediate tif- & png-files converted from NDPI-files.\"; + #rm -v *x40*.tif; + #rm -v *x40*.png; + fi; + echo \"Removing list of files to process.\"; + #rm -v files2cp.txt; + echo \"Gzipping result files.\"; + gzip -vf cp_output/${STAIN}*.gct; + gzip -vf cp_output/${STAIN}*.csv; + " | qsub -cwd -N ${THIS_WRAPUP} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_CELLPROFILER} -m ${MAILSETTING} -M ${EMAIL} + + cd .. + + INDEX=$((INDEX+1)) + if [ "$INDEX" -eq "$BATCH_SIZE" ]; then + break; + fi; + done + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message diff --git a/legacy/slideQuantifySGE b/legacy/slideQuantifySGE new file mode 100755 index 0000000..4737831 --- /dev/null +++ b/legacy/slideQuantifySGE @@ -0,0 +1,351 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #3 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #4 -- starting letters/characters of the image-subdirectories, e.g. IMG or AE or AAA." + echoerror "- Argument #5 -- output filename where the CellProfiler results are stored, e.g. Image.txt (delimiter is assumed '_')." + echoerror "- Argument #6 -- email address to send aborted/error jobs too, e.g. y.name@umcutrecht.nl." + echoerror "- Argument #7 -- by default emails are send when jobs are aborted/error, you can change that here, e.g. you can choose: b=begin of job; e=end of job; a=abort of job; s=suspended job; n=no mail is send." + echoerror "- Argument #8 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify [arg1: STAIN] [arg2: path_to_cellprofiler_pipeline] [arg3: path_to_working_directory] [arg4: IMG or AE or AAA] [arg5: Image.txt] [arg6: y.name@umcutrecht.nl] [arg7: a] [arg8: 210]" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-07-23" +echoitalic "* Version : 1.1.8" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +PIPELINE="$2" # Depends on arg2 +PROJECTDIR="$3" # Depends on arg3 +FILESTART="$4" # Depends on arg4 +OUTPUTFILENAME="$5" # Depends on arg5 +EMAIL="$6" # Depends on arg6 +MAILSETTING="$7" # Depends on arg7 +EMASKTHRESHOLD="$8" # Depends on arg8 + +QJOB_MEM_SLIDEMASK="100G" +QJOB_TIME_SLIDEMASK="02:30:00" + +QJOB_MEM_SLIDE2TILES="150G" +QJOB_TIME_SLIDE2TILES="04:30:00" + +QJOB_MEM_SLIDENORM="5G" +QJOB_TIME_SLIDENORM="02:00:00" + +QJOB_MEM_CP="10G" +QJOB_TIME_CP="09:00:00" + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 8 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify ***!" + +else + echobold "Cleaning up house, first." + mkdir -pv "$PROJECTDIR"/logs + mkdir -pv "$PROJECTDIR"/errors + rm -rfv "$FILESTART"*/magick-tmp + + ENTROPY_THRESHOLD="$EMASKTHRESHOLD" # should be argument + HOLD_JIDS=() + INDEX=0 + BATCH_SIZE=5000 # should be argument, but how exactly does this work? + UNIQUE_LABEL="V3" # should be argument + + ### FOR DEBUG + ### DEBUGLIST="AE999.FIBRIN AE99.T02-18575.A.FIBRIN AE997.T05-12460.FIBRIN" + ### for SLIDE_NUM in $DEBUGLIST; do + for SLIDE_NUM in "$FILESTART"*; do + sleep 1 + THIS_SLIDE=${SLIDE_NUM/\//} + THIS_SLIDEMASK=${THIS_SLIDE}_slideMask_${UNIQUE_LABEL} + THIS_SLIDE2TILES=${THIS_SLIDE}_slide2Tiles_${UNIQUE_LABEL} + THIS_SLIDENORMALIZE=${THIS_SLIDE}_slideNormalize_${UNIQUE_LABEL} + THIS_CELLPROFILER=${THIS_SLIDE}_CellProfiler_${UNIQUE_LABEL} + THIS_WRAPUP=${THIS_SLIDE}_WrapUp_${UNIQUE_LABEL} + + HOLD_JID_INDEX=$(expr ${INDEX} % ${BATCH_SIZE}) + + # process batches in smaller chunks + if [ "$INDEX" -lt "$BATCH_SIZE" ]; then + HOLD_JID_STRING="" + else + HOLD_JID_STRING="-hold_jid ${HOLD_JIDS[$HOLD_JID_INDEX]}" + fi + HOLD_JIDS[$HOLD_JID_INDEX]=$THIS_CELLPROFILER + + cd "$SLIDE_NUM" || exit + echo "" + echobold "Processing images of file [ ${SLIDE_NUM} ] for staining [ ${STAIN} ] -- at index [ ${INDEX} ] with total batch size [ ${BATCH_SIZE} ]." + + if [[ -s cp_output/"$STAIN"_Image.csv.gz ]]; then + echoitalic "Checking if this file was processed before; cp_output/${STAIN}_Image.csv.gz should be present ....." + ls -lh cp_output/*.gz + cd .. ; + INDEX=$((INDEX+1)); + echo "..... Analysis was already done!"; + continue; + fi + + echo "..... Processing file for the first time. Submitting job for: " + + rm -rfv "$PROJECTDIR"/logs/"$SLIDE_NUM"* + rm -rfv "$PROJECTDIR"/errors/"$SLIDE_NUM"* + + ### slideEMask for macro, and NDPISplit when necessary + echo "" + echoitalic "* slideEMask, which will mask, create a macro, and do NDPISplit when necessary." + + echo "#if [ -s *mask.png ]; then exit; fi; + module load slideToolkit + module load ndpitools + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + if [ -f *.ndpi ]; then + echo \"The image-file is a NDPI and will be converted to .tif before masking.\" + if [ -f *.ndpi ]; then ndpisplit -x40 -z0 *.ndpi; fi + slideMask --layer 0 -f *.tif; + + elif [ -f *.tif ]; then + echo \"The image-file was NDPI and converted to .tif.\" + slideMask --layer 0 -f *.tif; + + elif [ -f *.TIF ]; then + echo \"The image-file is a .TIF.\" + slideMask --layer 3 -f *.TIF; + + else + echoerrorflash \"*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF' ]\" + exit 1 + fi + slideEMask -f *macro.png -t $ENTROPY_THRESHOLD + rm -rfv magick-tmp + " | qsub -cwd -N ${THIS_SLIDEMASK} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ ${HOLD_JID_STRING} -l h_vmem=${QJOB_MEM_SLIDEMASK} -l h_rt=${QJOB_TIME_SLIDEMASK} -m ${MAILSETTING} -M ${EMAIL} + ### FOR DEBUG + ### echo $THIS_SLIDEMASK + ### echo $HOLD_JID_STRING + + ### slide2Tiles + echo "" + echoitalic "* slide2Tiles to create tiles from images." + + echo "if [ -d *.tiles ]; then exit; fi; + module load ndpitools; + module load slideToolkit; + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + if [ -f *.ndpi ]; then + echo \"The image-file is a NDPI and will be converted to .tif before tiling.\" + slide2Tiles --layer 0 -f *x40*.tif -m *emask.png; + + elif [ -f *.tif ]; then + echo \"The image-file was NDPI and converted to .tif.\" + slide2Tiles --layer 0 -f *.tif -m *.emask.png; + + elif [ -f *.TIF ]; then + echo \"The image-file is a .TIF.\" + slide2Tiles --layer 3 -f *.TIF -m *.emask.png; + + else + echoerrorflash \"*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF' ]\" + exit 1 + fi + rm -rfv magick-tmp + " | qsub -cwd -N ${THIS_SLIDE2TILES} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDEMASK} -l h_vmem=${QJOB_MEM_SLIDE2TILES} -l h_rt=${QJOB_TIME_SLIDE2TILES} -m ${MAILSETTING} -M ${EMAIL} + + ### slideNormalize + echo "" + echoitalic "* slideNormalize for normalization of images." + + echo "if [ -s files2cp.txt ]; then exit; fi; + if [ ! -d *.tiles ]; then + (>&2 echo "*** ERROR *** No tiles to process. ") + exit; + fi + cd *.tiles/; + module load slideNormalize; + module load slideToolkit; + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + for f in *.png; do + echo \"...Processing tile \$f\"; + slideNormalize \$f; + slideEMask -c -f \$f -t $ENTROPY_THRESHOLD + rm -v \$f; + done + rm -rfv magick-tmp + cd .. + ls -d -1 \$PWD/*tiles/*normalized* > files2cp.txt; + ls -d -1 \$PWD/*tiles/ENTROPY* >> files2cp.txt; + " | qsub -cwd -N ${THIS_SLIDENORMALIZE} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDE2TILES} -l h_vmem=${QJOB_MEM_SLIDENORM} -l h_rt=${QJOB_TIME_SLIDENORM} -m ${MAILSETTING} -M ${EMAIL} + + ### CellProfiler + echo "" + echoitalic "* CellProfiler to analyze images." + + echo "if [ -d cp_output ]; then exit; fi; + module load anaconda + mkdir -pv cp_output + cellprofiler -c -r -p $PIPELINE --file-list files2cp.txt -o cp_output/; + " | qsub -cwd -N ${THIS_CELLPROFILER} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_SLIDENORMALIZE} -l h_vmem=${QJOB_MEM_CP} -l h_rt=${QJOB_TIME_CP} -m ${MAILSETTING} -M ${EMAIL} + + ### REMOVE THIS BREAK FOR PRODUCTION! + ### When this break is active, only the first item + ### in the list of items to be processed will be processed. +# INDEX=$((INDEX+1)) +# break; + + ### Wrap Up + echo "" + echoitalic "* WrapUp the work we have done, and clean house." + + # Obsolete code + # 1. we randomly grap 10 masked & tiled PNG-files to keep for post-analysis check >> OUT-COMMENTED + # 2. we get the data (in STAIN_Image.txt) for each study number (SLIDE_NUM) in 'cp_output' using >> OUT-COMMENTED + # Colsums.R + # 3. we remove tiles, and other files we don't need + + echo " + ls cp_output/*.png | shuf -n \$(expr \$(ls cp_output/*.png | wc -l) - 10) | xargs rm; + echo 'SLIDE_NUM $STAIN' > results.txt; + cd cp_output; + SLIDE_NUM=\$(basename \"$PWD\") | cut -d'.' -f1 + echo $SLIDE_NUM \$(Rscript $SLIDETOOLKITDIR/utilities/Colsums.R $STAIN $OUTPUTFILENAME) >> ../results.txt; + cd .. + rm -rfv *tiles/; + if [ -f *.ndpi ]; then + echo \"Removing intermediate tif- & png-files converted from NDPI-files.\"; + rm -v *x40*.tif; + rm -v *x40*.png; + fi; + echo \"Removing list of files to process.\"; + rm -v files2cp.txt; + echo \"Gzipping result files.\"; + gzip -vf cp_output/${STAIN}*.gct; + gzip -vf cp_output/${STAIN}*.csv; + " | qsub -cwd -N ${THIS_WRAPUP} -e ${PROJECTDIR}/errors/ -o ${PROJECTDIR}/logs/ -hold_jid ${THIS_CELLPROFILER} -m ${MAILSETTING} -M ${EMAIL} + + cd .. + + INDEX=$((INDEX+1)) + if [ "$INDEX" -eq "$BATCH_SIZE" ]; then + break; + fi; + done + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message diff --git a/pipeLines/CD34.DAB.cp413.v1.0.cppipe b/pipeLines/CD34.DAB.cp413.v1.0.cppipe new file mode 100644 index 0000000..b18ecb3 --- /dev/null +++ b/pipeLines/CD34.DAB.cp413.v1.0.cppipe @@ -0,0 +1,401 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y', 'EntropyImage': 'Y'}, {'Original': 'X', 'EntropyImage': 'X'}, {'Original': 'NR', 'EntropyImage': 'NR'}, {'Original': 'STAIN', 'EntropyImage': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, timelapse movies, etc.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +UnmixColors:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find tissue using the grayscale image.', 'White is background, nonwhite is foreground.', 'Diameter is choosen after some testing.', 'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded areas in contrast to Threshold.', 'From now on, outlined foreground is called Tissue areas (black).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:9|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Hematoxylin (nuclei) positive areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,10000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find DAB positive nuclei (red) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:14|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find HE positive nuclei (blue) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +OverlayOutlines:[module_num:15|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:Blue + Select objects to display:nuclei_HE + Select outline color:#21FF06 + Select objects to display:HE_object + Select outline color:#FB0207 + Select objects to display:HE_object + Select outline color:#FFFF0A + Select objects to display:DAB_object + Select outline color:#118002 + Select objects to display:Tissue_object + +SaveImages:[module_num:16|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:17|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:DAB_object, HE_object, Tissue_object, nuclei_DAB, nuclei_HE + +ExportToSpreadsheet:[module_num:18|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_DAB_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD34.DAB.cp413.v1.0.cpproj b/pipeLines/CD34.DAB.cp413.v1.0.cpproj new file mode 100644 index 0000000..897faac Binary files /dev/null and b/pipeLines/CD34.DAB.cp413.v1.0.cpproj differ diff --git a/pipeLines/CD34.LRP.cp413.v1.0.cppipe b/pipeLines/CD34.LRP.cp413.v1.0.cppipe new file mode 100644 index 0000000..2adaf4d --- /dev/null +++ b/pipeLines/CD34.LRP.cp413.v1.0.cppipe @@ -0,0 +1,274 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{'Original': 'Y', 'EntropyImage': 'Y'}, {'Original': 'X', 'EntropyImage': 'X'}, {'Original': 'NR', 'EntropyImage': 'NR'}, {'Original': 'STAIN', 'EntropyImage': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OriginalGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total tissue area (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.165 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Split CD34 positive staining into separate channels (RED: LRP based CD34 staining; DAB for DAB based CD34 staining).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:LPR + Stain:Fast red + Red absorbance:0.8301 + Green absorbance:0.3273 + Blue absorbance:0.2568 + Name the output image:DAB + Stain:Feulgen + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total CD34 area (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:LPR + Name the primary objects to be identified:LPR_object + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0.7 + Lower and upper bounds on threshold:0.55,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue_object + Select outline color:#FB0207 + Select objects to display:LPR_object + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:LPR_object_yellow, Tissue_object_black + Select object sets to measure:LPR_object, Tissue_object + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder|Desktop/CD34/cp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_LRP_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD34.LRP.cp413.v1.0.cpproj b/pipeLines/CD34.LRP.cp413.v1.0.cpproj new file mode 100644 index 0000000..a82edea Binary files /dev/null and b/pipeLines/CD34.LRP.cp413.v1.0.cpproj differ diff --git a/pipeLines/CD3_CD56_NKT.cp413.v2.1.cppipe b/pipeLines/CD3_CD56_NKT.cp413.v2.1.cppipe new file mode 100644 index 0000000..1f64477 --- /dev/null +++ b/pipeLines/CD3_CD56_NKT.cp413.v2.1.cppipe @@ -0,0 +1,304 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") (file doesnot contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PCD3_CD56_NKT).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Color image + Name to assign these images:original + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:6 + Select the input color image:Original + Name the output image:CD3_HDY_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output image:HE + Stain:Hematoxylin + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output image:CD3_CD56_NKT_orange + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output image:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output image:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + Name the output image:CD56_LPR_red + Stain:Fast red + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_nuclei + Typical diameter of objects, in pixel units (Min,Max):4,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD3_CD56_NKT_orange + Name the primary objects to be identified:CD3_CD56_NKT_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD3_HDY_yellow + Name the primary objects to be identified:CD3_HDY_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:9|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD56_LPR_red + Name the primary objects to be identified:CD56_LPR_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +MeasureImageAreaOccupied:[module_num:10|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:CD3_CD56_NKT_cells, CD3_HDY_cells, CD56_LPR_cells, HE_nuclei + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#EB6732 + Select objects to display:CD3_CD56_NKT_cells + Select outline color:#FFFF0A + Select objects to display:CD3_HDY_cells + Select outline color:#FB0207 + Select objects to display:CD56_LPR_cells + Select outline color:#21FF06 + Select objects to display:HE_nuclei + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +ExportToSpreadsheet:[module_num:13|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:Yes + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder|Desktop/NKT images/colorsetting + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_CD56_NKT_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD3_CD56_NKT.cp413.v2.cppipe b/pipeLines/CD3_CD56_NKT.cp413.v2.cppipe new file mode 100644 index 0000000..63058dd --- /dev/null +++ b/pipeLines/CD3_CD56_NKT.cp413.v2.cppipe @@ -0,0 +1,304 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") (file doesnot contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Color image + Name to assign these images:original + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:6 + Select the input color image:Original + Name the output image:CD3_HDY_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output image:HE + Stain:Hematoxylin + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output image:CD3_CD56_NKT_orange + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output image:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output image:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + Name the output image:CD56_LPR_red + Stain:Fast red + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_nuclei + Typical diameter of objects, in pixel units (Min,Max):4,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD3_CD56_NKT_orange + Name the primary objects to be identified:CD3_CD56_NKT_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD3_HDY_yellow + Name the primary objects to be identified:CD3_HDY_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:9|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:CD56_LPR_red + Name the primary objects to be identified:CD56_LPR_cells + Typical diameter of objects, in pixel units (Min,Max):4,20000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +MeasureImageAreaOccupied:[module_num:10|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:CD3_CD56_NKT_cells, CD3_HDY_cells, CD56_LPR_cells, HE_nuclei + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#EB6732 + Select objects to display:CD3_CD56_NKT_cells + Select outline color:#FFFF0A + Select objects to display:CD3_HDY_cells + Select outline color:#FB0207 + Select objects to display:CD56_LPR_cells + Select outline color:#21FF06 + Select objects to display:HE_nuclei + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +ExportToSpreadsheet:[module_num:13|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:Yes + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder|Desktop/NKT images/colorsetting + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_CD56_NKT_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD3_CD56_NKT.cp413.v2.cpproj b/pipeLines/CD3_CD56_NKT.cp413.v2.cpproj new file mode 100644 index 0000000..4d4854e Binary files /dev/null and b/pipeLines/CD3_CD56_NKT.cp413.v2.cpproj differ diff --git a/pipeLines/CD66b.cp413.v1.cppipe b/pipeLines/CD66b.cp413.v1.cppipe new file mode 100644 index 0000000..4e2fc59 --- /dev/null +++ b/pipeLines/CD66b.cp413.v1.cppipe @@ -0,0 +1,332 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file doesnot contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y'}, {'Original': 'X'}, {'Original': 'NR'}, {'Original': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +Smooth:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Tissue areas (black)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Manual threshold:0.05 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse HE in the previously defined Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:13|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_stain_image + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify nuclei (green) in DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_stain_image + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):2,10 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +OverlayOutlines:[module_num:15|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one. DAB nuclei are red, DAB areas are yellow, Tissue is green.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#FB0207 + Select objects to display:DAB_Nuclei_object + Select outline color:#FFFF0A + Select objects to display:DAB_object + Select outline color:#118002 + Select objects to display:Tissue_object + +SaveImages:[module_num:16|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:17|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure Tissue (black) and DAB-HE nuclei (green) and DAB area (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:DAB, Hematoxylin, OriginalGray + Select object sets to measure:DAB_Nuclei_object, DAB_object, Tissue_object + +ExportToSpreadsheet:[module_num:18|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Tissue_object|Number_Object_Number,Tissue_object|Location_Center_Y,Tissue_object|Location_Center_X,Image|Count_Tissue_object,Image|Count_DAB_object,Image|AreaOccupied_Perimeter_Tissue_object_green,Image|AreaOccupied_Perimeter_DAB_object_yellow,Image|AreaOccupied_AreaOccupied_Tissue_object_green,Image|AreaOccupied_AreaOccupied_DAB_object_yellow,Image|AreaOccupied_TotalArea_Tissue_object_green,Image|AreaOccupied_TotalArea_DAB_object_yellow,Image|Width_Original,Image|FileName_Original,Image|Height_Original,DAB_object|Location_Center_Y,DAB_object|Location_Center_X,DAB_object|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD66b.cp413.v1.cpproj b/pipeLines/CD66b.cp413.v1.cpproj new file mode 100644 index 0000000..1dc9210 Binary files /dev/null and b/pipeLines/CD66b.cp413.v1.cpproj differ diff --git a/pipeLines/CD68.cp413.v1.0.cppipe b/pipeLines/CD68.cp413.v1.0.cppipe new file mode 100644 index 0000000..d03d831 --- /dev/null +++ b/pipeLines/CD68.cp413.v1.0.cppipe @@ -0,0 +1,401 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PCD68).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PCD68).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y', 'EntropyImage': 'Y'}, {'Original': 'X', 'EntropyImage': 'X'}, {'Original': 'NR', 'EntropyImage': 'NR'}, {'Original': 'STAIN', 'EntropyImage': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +UnmixColors:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find tissue using the grayscale image.', 'White is background, non-white is foreground.', 'Diameter is choosen after some testing.', 'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded areas in contrast to Threshold.', 'From now on, outlined foreground is called Tissue areas (black).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:9|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Hematoxylin (nuclei) positive areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find DAB positive nuclei (red) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:14|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find HE positive nuclei (blue) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +OverlayOutlines:[module_num:15|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:Blue + Select objects to display:nuclei_HE + Select outline color:Yellow + Select objects to display:DAB_object + Select outline color:Red + Select objects to display:nuclei_DAB + Select outline color:Green + Select objects to display:HE_object + Select outline color:black + Select objects to display:Tissue_object + +SaveImages:[module_num:16|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:17|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Binary Image + Select binary images to measure:DAB, DAB_object_mask, HE_object_mask, Hematoxylin, Tissue_object + Select object sets to measure:DAB_object, HE_object, Tissue_object, nuclei_DAB, nuclei_HE + +ExportToSpreadsheet:[module_num:18|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/CD68.cp413.v1.0.cpproj b/pipeLines/CD68.cp413.v1.0.cpproj new file mode 100644 index 0000000..d899334 Binary files /dev/null and b/pipeLines/CD68.cp413.v1.0.cpproj differ diff --git a/pipeLines/CD68.cp413.v1.0.txt b/pipeLines/CD68.cp413.v1.0.txt new file mode 100644 index 0000000..0cd8f08 --- /dev/null +++ b/pipeLines/CD68.cp413.v1.0.txt @@ -0,0 +1,66 @@ +[ 1] [Images] + To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders. + + Settings converted from legacy pipeline. + +[ 2] [Metadata] + The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file. + + Settings converted from legacy pipeline. + +[ 3] [NamesAndTypes] + The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it. + + Settings converted from legacy pipeline. + Please change any color images from Load as Grayscale image to Load as Color image. + +[ 4] [Groups] + The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc. + + Settings converted from legacy pipeline. + +[ 5] [ColorToGray] + Create gray channel, this is used to find tissue (green) + Red is ussually a good channel for tissue. + RGB should also be fine. + +[ 6] [ImageMath] + +[ 7] [UnmixColors] + Split HE and DAB into separate channels + +[ 8] [IdentifyPrimaryObjects] + Find tissue using the grayscale image. + White is background, non-white is foreground. + Diameter is choosen after some testing. + Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded areas in contrast to Threshold. + From now on, outlined foreground is called Tissue areas (black). + +[ 9] [IdentifyPrimaryObjects] + Identify Hematoxylin (nuclei) positive areas (green) + +[ 10] [IdentifyPrimaryObjects] + Identify DAB positive areas (yellow) + +[ 11] [MaskImage] + Only analyse Nuclei (red) in the previously defined DAB tissue (yellow) + +[ 12] [MaskImage] + +[ 13] [IdentifyPrimaryObjects] + Find DAB positive nuclei (red) within masked image (green) + +[ 14] [IdentifyPrimaryObjects] + Find HE positive nuclei (blue) within masked image (green) + +[ 15] [OverlayOutlines] + The next outline is ontop of the previous one. + +[ 16] [SaveImages] + Save outlined images + These image are usually not used for further analysis and can be stored in lossy JPEG if wanted. + +[ 17] [MeasureImageAreaOccupied] + Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area + +[ 18] [ExportToSpreadsheet] diff --git a/pipeLines/EvG.cp413.v1.0.cppipe b/pipeLines/EvG.cp413.v1.0.cppipe new file mode 100644 index 0000000..5e7da5a --- /dev/null +++ b/pipeLines/EvG.cp413.v1.0.cppipe @@ -0,0 +1,274 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{u'Original': u'Y', u'EntropyImage': u'Y'}, {u'Original': u'X', u'EntropyImage': u'X'}, {u'Original': u'NR', u'EntropyImage': u'NR'}, {u'Original': u'STAIN', u'EntropyImage': u'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total tissue area (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.165 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:EVG + Stain:Custom + Red absorbance:0.20333 + Green absorbance:0.51696 + Blue absorbance:0.505697 + Name the output image:EVG_IDENTIFYING + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total EVG area (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:EVG_IDENTIFYING + Name the primary objects to be identified:EVG_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,10000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:.8 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure total tissue area (green) and total EVG positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#107F01 + Select objects to display:Tissue + Select outline color:#FB0106 + Select objects to display:EVG_Tissue + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure total tissue area (green) and total EvG positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:EVG_IDENTIFYING, OrigGray + Select object sets to measure:EVG_Tissue, Tissue + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder|Desktop/FIBRINTEST/cp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:EVG_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/EvG.cp413.v1.0.cpproj b/pipeLines/EvG.cp413.v1.0.cpproj new file mode 100644 index 0000000..39bc528 Binary files /dev/null and b/pipeLines/EvG.cp413.v1.0.cpproj differ diff --git a/pipeLines/FIBRIN.cp413.v1.0.cppipe b/pipeLines/FIBRIN.cp413.v1.0.cppipe new file mode 100644 index 0000000..d06912a --- /dev/null +++ b/pipeLines/FIBRIN.cp413.v1.0.cppipe @@ -0,0 +1,269 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{u'Original': u'Y', u'EntropyImage': u'Y'}, {u'Original': u'X', u'EntropyImage': u'X'}, {u'Original': u'NR', u'EntropyImage': u'NR'}, {u'Original': u'STAIN', u'EntropyImage': u'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.165 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output image:FIBRIN + Stain:Custom + Red absorbance:0.345301 + Green absorbance:0.854273 + Blue absorbance:0.388568 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:FIBRIN + Name the primary objects to be identified:Fibrin_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.6,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#107F40 + Select objects to display:Tissue + Select outline color:#FB0106 + Select objects to display:Fibrin_Tissue + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure total tissue area (green) and total Fibrin positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:Tissue, Fibrin_Tissue + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:FIBRIN_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/FIBRIN.cp413.v1.0.cpproj b/pipeLines/FIBRIN.cp413.v1.0.cpproj new file mode 100644 index 0000000..6f36c63 Binary files /dev/null and b/pipeLines/FIBRIN.cp413.v1.0.cpproj differ diff --git a/pipeLines/GLYCC.cp413.v0.9.cppipe b/pipeLines/GLYCC.cp413.v0.9.cppipe new file mode 100644 index 0000000..4a9ab72 --- /dev/null +++ b/pipeLines/GLYCC.cp413.v0.9.cppipe @@ -0,0 +1,262 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:16 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\\d*?)\\.(?P(UMC|T.*?))\\.(?P.*?)\\.(?P\\d*?)\\.X(?P\\d+)\\.Y(?P\\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y'}, {'Original': 'X'}, {'Original': 'NR'}, {'Original': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Tissue areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Manual threshold:0.05 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +UnmixColors:[module_num:8|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse HE in the previously defined Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Hematoxylin + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_Masked_DAB_channel + Invert the mask?:No + +OverlayOutlines:[module_num:13|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue_object + Select outline color:#FB0207 + Select objects to display:DAB_object + +SaveImages:[module_num:14|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:15|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Binary Image + Select binary images to measure:DAB, DAB_object_mask, Hematoxylin, OrigOverlay, Original, OriginalGray, OriginalGray_smooth, Tissue_Masked_DAB_channel, Tissue_Masked_HE_channel + Select object sets to measure:DAB_object, Tissue_object + +ExportToSpreadsheet:[module_num:16|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Tissue_object|Number_Object_Number,Tissue_object|Location_Center_Y,Tissue_object|Location_Center_X,Image|Count_Tissue_object,Image|Count_DAB_object,Image|AreaOccupied_Perimeter_Tissue_object_green,Image|AreaOccupied_Perimeter_DAB_object_yellow,Image|AreaOccupied_AreaOccupied_Tissue_object_green,Image|AreaOccupied_AreaOccupied_DAB_object_yellow,Image|AreaOccupied_TotalArea_Tissue_object_green,Image|AreaOccupied_TotalArea_DAB_object_yellow,Image|Width_Original,Image|FileName_Original,Image|Height_Original,DAB_object|Location_Center_Y,DAB_object|Location_Center_X,DAB_object|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/GLYCC.cp413.v0.9.cpproj b/pipeLines/GLYCC.cp413.v0.9.cpproj new file mode 100644 index 0000000..b51695d Binary files /dev/null and b/pipeLines/GLYCC.cp413.v0.9.cpproj differ diff --git a/pipeLines/GLYCC.cp413.v1.0.cppipe b/pipeLines/GLYCC.cp413.v1.0.cppipe new file mode 100644 index 0000000..1612041 --- /dev/null +++ b/pipeLines/GLYCC.cp413.v1.0.cppipe @@ -0,0 +1,281 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".normalized.tile.tissue.png") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PGLYCC).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y'}, {'Original': 'X'}, {'Original': 'NR'}, {'Original': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', ' ', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +Smooth:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Tissue areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Manual threshold:0.05 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Image + Select object for mask:Tissue_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse HE in the previously defined Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Image + Select object for mask:Tissue_object + Select image for mask:Hematoxylin + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:13|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Image + Select object for mask:DAB_object + Select image for mask:Tissue_Masked_DAB_channel + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue_object + Select outline color:#FB0207 + Select objects to display:DAB_object + +SaveImages:[module_num:15|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:16|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:DAB_object_mask, Tissue_Masked_DAB_channel, Tissue_Masked_HE_channel + Select object sets to measure:DAB_object, Tissue_object + +ExportToSpreadsheet:[module_num:17|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Tissue_object|Number_Object_Number,Tissue_object|Location_Center_Y,Tissue_object|Location_Center_X,Image|Count_Tissue_object,Image|Count_DAB_object,Image|AreaOccupied_Perimeter_Tissue_object_green,Image|AreaOccupied_Perimeter_DAB_object_yellow,Image|AreaOccupied_AreaOccupied_Tissue_object_green,Image|AreaOccupied_AreaOccupied_DAB_object_yellow,Image|AreaOccupied_TotalArea_Tissue_object_green,Image|AreaOccupied_TotalArea_DAB_object_yellow,Image|Width_Original,Image|FileName_Original,Image|Height_Original,DAB_object|Location_Center_Y,DAB_object|Location_Center_X,DAB_object|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/GLYCC.cp413.v1.0.cpproj b/pipeLines/GLYCC.cp413.v1.0.cpproj new file mode 100644 index 0000000..86b7870 Binary files /dev/null and b/pipeLines/GLYCC.cp413.v1.0.cpproj differ diff --git a/pipeLines/GLYCC.cp413.v1.0.imageset_tested.csv b/pipeLines/GLYCC.cp413.v1.0.imageset_tested.csv new file mode 100644 index 0000000..9ac21df --- /dev/null +++ b/pipeLines/GLYCC.cp413.v1.0.imageset_tested.csv @@ -0,0 +1,5 @@ +"Group_Number","Group_Index","URL_Original","PathName_Original","FileName_Original","Series_Original","Frame_Original","Channel_Original","Metadata_Date","Metadata_FileLocation","Metadata_Frame","Metadata_NR","Metadata_STAIN","Metadata_Series","Metadata_T_NUMBER","Metadata_X","Metadata_Y" +1,1,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.GLYCC.X1.Y2.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles","AE1468.GLYCC.X1.Y2.normalized.tile.tissue.png",0,0,-2,"","file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.GLYCC.X1.Y2.normalized.tile.tissue.png","0","AE1468","GLYCC","0","","X1","Y2" +1,2,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.T14-12345.GLYCC.20211107.X1.Y2.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles","AE1468.T14-12345.GLYCC.20211107.X1.Y2.normalized.tile.tissue.png",0,0,-2,"20211107","file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.T14-12345.GLYCC.20211107.X1.Y2.normalized.tile.tissue.png","0","AE1468","GLYCC","0","T14-12345","X1","Y2" +1,3,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.T14-12345.GLYCC.X1.Y2.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles","AE1468.T14-12345.GLYCC.X1.Y2.normalized.tile.tissue.png",0,0,-2,"","file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.T14-12345.GLYCC.X1.Y2.normalized.tile.tissue.png","0","AE1468","GLYCC","0","T14-12345","X1","Y2" +1,4,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.UMC.GLYCC.X1.Y2.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles","AE1468.UMC.GLYCC.X1.Y2.normalized.tile.tissue.png",0,0,-2,"","file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/GLYCC/AE1468.GLYCC/AE1468.GLYCC.tiles/AE1468.UMC.GLYCC.X1.Y2.normalized.tile.tissue.png","0","AE1468","GLYCC","0","UMC","X1","Y2" diff --git a/pipeLines/GLYCC.cp413.v1.0.txt b/pipeLines/GLYCC.cp413.v1.0.txt new file mode 100644 index 0000000..ce6f11d --- /dev/null +++ b/pipeLines/GLYCC.cp413.v1.0.txt @@ -0,0 +1,60 @@ +[ 1] [Images] + To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders. + + Settings converted from legacy pipeline. + +[ 2] [Metadata] + The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file. + + Settings converted from legacy pipeline. + +[ 3] [NamesAndTypes] + The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it. + + Settings converted from legacy pipeline. + Please change any color images from Load as Grayscale image to Load as Color image. + +[ 4] [Groups] + The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc. + + Settings converted from legacy pipeline. + +[ 5] [ColorToGray] + Create gray channel, this is used to find tissue (green) + Red is ussually a good channel for tissue. + RGB should also be fine. + +[ 6] [ImageMath] + +[ 7] [Smooth] + Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green) + +[ 8] [IdentifyPrimaryObjects] + Identify Tissue areas (green) + +[ 9] [UnmixColors] + Split HE and DAB into separate channels + +[ 10] [MaskImage] + Only analyse DAB in the previously defined Tissue + +[ 11] [MaskImage] + Only analyse HE in the previously defined Tissue (green) + +[ 12] [IdentifyPrimaryObjects] + Identify DAB positive areas (yellow) + +[ 13] [MaskImage] + Only analyse Nuclei (red) in the previously defined DAB tissue (yellow) + +[ 14] [OverlayOutlines] + The next outline is ontop of the previous one. + +[ 15] [SaveImages] + Save outlined images + These image are usually not used for further analysis and can be stored in lossy JPEG if wanted. + +[ 16] [MeasureImageAreaOccupied] + Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red). + +[ 17] [ExportToSpreadsheet] diff --git a/pipeLines/GLYCC.cp413.v1.1.cppipe b/pipeLines/GLYCC.cp413.v1.1.cppipe new file mode 100644 index 0000000..a105867 --- /dev/null +++ b/pipeLines/GLYCC.cp413.v1.1.cppipe @@ -0,0 +1,281 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".normalized.tile.tissue.png") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PGLYCC).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?P\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y'}, {'Original': 'X'}, {'Original': 'NR'}, {'Original': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', ' ', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +Smooth:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Tissue areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):500,4000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:20 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0,0.15 + Manual threshold:0.05 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Robust Background + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Image + Select object for mask:Tissue_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse HE in the previously defined Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Image + Select object for mask:Tissue_object + Select image for mask:Hematoxylin + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,4000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0,1 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:13|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Image + Select object for mask:DAB_object + Select image for mask:Tissue_Masked_DAB_channel + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue_object + Select outline color:#FB0207 + Select objects to display:DAB_object + +SaveImages:[module_num:15|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:16|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:DAB_object_mask, Tissue_Masked_DAB_channel, Tissue_Masked_HE_channel + Select object sets to measure:DAB_object, Tissue_object + +ExportToSpreadsheet:[module_num:17|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Tissue_object|Number_Object_Number,Tissue_object|Location_Center_Y,Tissue_object|Location_Center_X,Image|Count_Tissue_object,Image|Count_DAB_object,Image|AreaOccupied_Perimeter_Tissue_object_green,Image|AreaOccupied_Perimeter_DAB_object_yellow,Image|AreaOccupied_AreaOccupied_Tissue_object_green,Image|AreaOccupied_AreaOccupied_DAB_object_yellow,Image|AreaOccupied_TotalArea_Tissue_object_green,Image|AreaOccupied_TotalArea_DAB_object_yellow,Image|Width_Original,Image|FileName_Original,Image|Height_Original,DAB_object|Location_Center_Y,DAB_object|Location_Center_X,DAB_object|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/GLYCC.cp413.v1.1.cpproj b/pipeLines/GLYCC.cp413.v1.1.cpproj new file mode 100644 index 0000000..883d3e3 Binary files /dev/null and b/pipeLines/GLYCC.cp413.v1.1.cpproj differ diff --git a/pipeLines/GLYCC.cp413.v1.1.imageset_tested.csv b/pipeLines/GLYCC.cp413.v1.1.imageset_tested.csv new file mode 100644 index 0000000..04361f0 --- /dev/null +++ b/pipeLines/GLYCC.cp413.v1.1.imageset_tested.csv @@ -0,0 +1,50 @@ +"Group_Number","Group_Index","URL_Original","PathName_Original","FileName_Original","Series_Original","Frame_Original","Channel_Original","Metadata_Date","Metadata_FileLocation","Metadata_Frame","Metadata_NR","Metadata_STAIN","Metadata_Series","Metadata_TILENR","Metadata_T_NUMBER" 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You can also specify a set of rules to include only the desired files in your selected folders. + + Settings converted from legacy pipeline. + +[ 2] [Metadata] + The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file. + + Settings converted from legacy pipeline. + +[ 3] [NamesAndTypes] + The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it. + + Settings converted from legacy pipeline. + Please change any color images from Load as Grayscale image to Load as Color image. + +[ 4] [Groups] + The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc. + + Settings converted from legacy pipeline. + +[ 5] [ColorToGray] + Create gray channel, this is used to find tissue (green) + Red is ussually a good channel for tissue. + RGB should also be fine. + +[ 6] [ImageMath] + +[ 7] [Smooth] + Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green) + +[ 8] [IdentifyPrimaryObjects] + Identify Tissue areas (green) + +[ 9] [UnmixColors] + Split HE and DAB into separate channels + +[ 10] [MaskImage] + Only analyse DAB in the previously defined Tissue + +[ 11] [MaskImage] + Only analyse HE in the previously defined Tissue (green) + +[ 12] [IdentifyPrimaryObjects] + Identify DAB positive areas (yellow) + +[ 13] [MaskImage] + Only analyse Nuclei (red) in the previously defined DAB tissue (yellow) + +[ 14] [OverlayOutlines] + The next outline is ontop of the previous one. + +[ 15] [SaveImages] + Save outlined images + These image are usually not used for further analysis and can be stored in lossy JPEG if wanted. + +[ 16] [MeasureImageAreaOccupied] + Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red). + +[ 17] [ExportToSpreadsheet] diff --git a/pipeLines/HE.cp413.v0.9.cppipe b/pipeLines/HE.cp413.v0.9.cppipe new file mode 100644 index 0000000..6a14383 --- /dev/null +++ b/pipeLines/HE.cp413.v0.9.cppipe @@ -0,0 +1,274 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".normalized.tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PHE|HE_x\d+\_z\d+).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PHE|HE_x\d+\_z\d+).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{u'Original': u'Y', u'EntropyImage': u'Y'}, {u'Original': u'X', u'EntropyImage': u'X'}, {u'Original': u'NR', u'EntropyImage': u'NR'}, {u'Original': u'STAIN', u'EntropyImage': u'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green).', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:ImageAfterMath + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Invert the gray image, black becomes white, white becomes black.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:ImageAfterMath + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total tissue area (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.065 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Unmix the colors. Hematoxylin (blueish for nuclei) and Eosin (pinkish for tissue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:HE + Stain:Hematoxylin + Red absorbance:0.434522 + Green absorbance:0.757134 + Blue absorbance:0.487789 + Name the output image:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total HE area (blue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):12,36 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.70,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Minimum Cross-Entropy + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (blue)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue + Select outline color:#0000FF + Select objects to display:HE_Nuclei + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:Eosin, HE, OrigGray + Select object sets to measure:HE_Nuclei, Tissue + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Image|Metadata_FileLocation,Image|Metadata_T_NUMBER,Image|Metadata_X,Image|Metadata_Series,Image|Metadata_STAIN,Image|Metadata_Date,Image|Metadata_NR,Image|Metadata_Y,Image|Metadata_Frame,Image|Width_Original,Image|MD5Digest_Original,Image|AreaOccupied_Perimeter_HE_Nuclei,Image|AreaOccupied_Perimeter_Tissue,Image|AreaOccupied_TotalArea_HE_Nuclei,Image|AreaOccupied_TotalArea_Tissue,Image|AreaOccupied_AreaOccupied_HE_Nuclei,Image|AreaOccupied_AreaOccupied_Tissue,Image|Frame_Original,Image|Threshold_SumOfEntropies_HE_Nuclei,Image|Threshold_SumOfEntropies_Tissue,Image|Threshold_FinalThreshold_HE_Nuclei,Image|Threshold_FinalThreshold_Tissue,Image|Threshold_WeightedVariance_Tissue,Image|Threshold_WeightedVariance_HE_Nuclei,Image|Threshold_OrigThreshold_HE_Nuclei,Image|Threshold_OrigThreshold_Tissue,Image|PathName_Original,Image|Group_Index,Image|Group_Number,Image|Count_Tissue,Image|Count_HE_Nuclei,Image|ModuleError_10IdentifyPrimaryObjects,Image|ModuleError_03NamesAndTypes,Image|ModuleError_09UnmixColors,Image|ModuleError_07ImageMath,Image|ModuleError_06Smooth,Image|ModuleError_08IdentifyPrimaryObjects,Image|ModuleError_12SaveImages,Image|ModuleError_11OverlayOutlines,Image|ModuleError_05ColorToGray,Image|ModuleError_13MeasureImageAreaOccupied,Image|ModuleError_01Images,Image|ModuleError_02Metadata,Image|ModuleError_04Groups,Image|ExecutionTime_10IdentifyPrimaryObjects,Image|ExecutionTime_02Metadata,Image|ExecutionTime_06Smooth,Image|ExecutionTime_12SaveImages,Image|ExecutionTime_03NamesAndTypes,Image|ExecutionTime_11OverlayOutlines,Image|ExecutionTime_05ColorToGray,Image|ExecutionTime_01Images,Image|ExecutionTime_09UnmixColors,Image|ExecutionTime_13MeasureImageAreaOccupied,Image|ExecutionTime_07ImageMath,Image|ExecutionTime_08IdentifyPrimaryObjects,Image|ExecutionTime_04Groups,Image|Series_Original,Image|Height_Original,Image|URL_Original,Image|Scaling_Original,Image|FileName_Original,Experiment|CellProfiler_Version,Experiment|Modification_Timestamp,Experiment|Pipeline_Pipeline,Experiment|Run_Timestamp,Tissue|Location_Center_X,Tissue|Location_Center_Y,Tissue|Location_Center_Z,Tissue|Number_Object_Number,HE_Nuclei|Location_Center_Y,HE_Nuclei|Location_Center_X,HE_Nuclei|Location_Center_Z,HE_Nuclei|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:HE_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/HE.cp413.v1.0.cppipe b/pipeLines/HE.cp413.v1.0.cppipe new file mode 100644 index 0000000..521a127 --- /dev/null +++ b/pipeLines/HE.cp413.v1.0.cppipe @@ -0,0 +1,263 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".normalized.tile.tissue.png") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PHE|HE_x\d+\_z\d+).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".normalized.tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{u'Original': u'Y', u'EntropyImage': u'Y'}, {u'Original': u'X', u'EntropyImage': u'X'}, {u'Original': u'NR', u'EntropyImage': u'NR'}, {u'Original': u'STAIN', u'EntropyImage': u'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green).', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:ImageAfterMath + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Invert the gray image, black becomes white, white becomes black.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:ImageAfterMath + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total tissue area (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.065 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Unmix the colors. Hematoxylin (blueish for nuclei) and Eosin (pinkish for tissue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:HE + Stain:Hematoxylin + Red absorbance:0.434522 + Green absorbance:0.757134 + Blue absorbance:0.487789 + Name the output image:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total HE area (blue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):12,36 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.70,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Minimum Cross-Entropy + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (blue)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue + Select outline color:#0000FF + Select objects to display:HE_Nuclei + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:Eosin, HE, OrigGray + Select object sets to measure:HE_Nuclei, Tissue + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Image|Metadata_FileLocation,Image|Metadata_T_NUMBER,Image|Metadata_X,Image|Metadata_Series,Image|Metadata_STAIN,Image|Metadata_Date,Image|Metadata_NR,Image|Metadata_Y,Image|Metadata_Frame,Image|Width_Original,Image|MD5Digest_Original,Image|AreaOccupied_Perimeter_HE_Nuclei,Image|AreaOccupied_Perimeter_Tissue,Image|AreaOccupied_TotalArea_HE_Nuclei,Image|AreaOccupied_TotalArea_Tissue,Image|AreaOccupied_AreaOccupied_HE_Nuclei,Image|AreaOccupied_AreaOccupied_Tissue,Image|Frame_Original,Image|Threshold_SumOfEntropies_HE_Nuclei,Image|Threshold_SumOfEntropies_Tissue,Image|Threshold_FinalThreshold_HE_Nuclei,Image|Threshold_FinalThreshold_Tissue,Image|Threshold_WeightedVariance_Tissue,Image|Threshold_WeightedVariance_HE_Nuclei,Image|Threshold_OrigThreshold_HE_Nuclei,Image|Threshold_OrigThreshold_Tissue,Image|PathName_Original,Image|Group_Index,Image|Group_Number,Image|Count_Tissue,Image|Count_HE_Nuclei,Image|ModuleError_10IdentifyPrimaryObjects,Image|ModuleError_03NamesAndTypes,Image|ModuleError_09UnmixColors,Image|ModuleError_07ImageMath,Image|ModuleError_06Smooth,Image|ModuleError_08IdentifyPrimaryObjects,Image|ModuleError_12SaveImages,Image|ModuleError_11OverlayOutlines,Image|ModuleError_05ColorToGray,Image|ModuleError_13MeasureImageAreaOccupied,Image|ModuleError_01Images,Image|ModuleError_02Metadata,Image|ModuleError_04Groups,Image|ExecutionTime_10IdentifyPrimaryObjects,Image|ExecutionTime_02Metadata,Image|ExecutionTime_06Smooth,Image|ExecutionTime_12SaveImages,Image|ExecutionTime_03NamesAndTypes,Image|ExecutionTime_11OverlayOutlines,Image|ExecutionTime_05ColorToGray,Image|ExecutionTime_01Images,Image|ExecutionTime_09UnmixColors,Image|ExecutionTime_13MeasureImageAreaOccupied,Image|ExecutionTime_07ImageMath,Image|ExecutionTime_08IdentifyPrimaryObjects,Image|ExecutionTime_04Groups,Image|Series_Original,Image|Height_Original,Image|URL_Original,Image|Scaling_Original,Image|FileName_Original,Experiment|CellProfiler_Version,Experiment|Modification_Timestamp,Experiment|Pipeline_Pipeline,Experiment|Run_Timestamp,Tissue|Location_Center_X,Tissue|Location_Center_Y,Tissue|Location_Center_Z,Tissue|Number_Object_Number,HE_Nuclei|Location_Center_Y,HE_Nuclei|Location_Center_X,HE_Nuclei|Location_Center_Z,HE_Nuclei|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:HE_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/HE.cp413.v1.cpproj b/pipeLines/HE.cp413.v1.cpproj new file mode 100644 index 0000000..1630012 Binary files /dev/null and b/pipeLines/HE.cp413.v1.cpproj differ diff --git a/pipeLines/HE.cp413.v1.imageset_tested.csv b/pipeLines/HE.cp413.v1.imageset_tested.csv new file mode 100644 index 0000000..3a6b4b0 --- /dev/null +++ b/pipeLines/HE.cp413.v1.imageset_tested.csv @@ -0,0 +1,4 @@ +"Group_Number","Group_Index","URL_Original","PathName_Original","FileName_Original","Series_Original","Frame_Original","Channel_Original","Metadata_Date","Metadata_FileLocation","Metadata_Frame","Metadata_NR","Metadata_STAIN","Metadata_Series","Metadata_T_NUMBER","Metadata_X","Metadata_Y" +1,1,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow","AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png",0,0,-2,nan,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png","0","AE1468","HE_x20_z0","0","UMC","X1","Y5" +1,2,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow","AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png",0,0,-2,nan,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png","0","AE1468","HE_x20_z0","0","UMC","X2","Y1" +1,3,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png","/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow","AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png",0,0,-2,nan,"file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png","0","AE1468","HE_x20_z0","0","UMC","X3","Y8" diff --git a/pipeLines/HE.cp413.v1.txt b/pipeLines/HE.cp413.v1.txt new file mode 100644 index 0000000..2e67b88 --- /dev/null +++ b/pipeLines/HE.cp413.v1.txt @@ -0,0 +1,41 @@ +[ 1] [Images] + To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders. + +[ 2] [Metadata] + The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file. + +[ 3] [NamesAndTypes] + The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it. + +[ 4] [Groups] + The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc. + +[ 5] [ColorToGray] + Create gray channel, this is used to find tissue (green). + Red is ussually a good channel for tissue. + RGB should also be fine. + +[ 6] [Smooth] + Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green). + +[ 7] [ImageMath] + Invert the gray image, black becomes white, white becomes black. + +[ 8] [IdentifyPrimaryObjects] + Identify total tissue area (green). + +[ 9] [UnmixColors] + Unmix the colors. Hematoxylin (blueish for nuclei) and Eosin (pinkish for tissue). + +[ 10] [IdentifyPrimaryObjects] + Identify total HE area (blue). + +[ 11] [OverlayOutlines] + Measure total tissue area (green) and total HE positive stain (blue) + +[ 12] [SaveImages] + +[ 13] [MeasureImageAreaOccupied] + Measure total tissue area (green) and total HE positive stain (red) + +[ 14] [ExportToSpreadsheet] diff --git a/pipeLines/SMA.cp413.v1.0.cppipe b/pipeLines/SMA.cp413.v1.0.cppipe new file mode 100644 index 0000000..6d63f16 --- /dev/null +++ b/pipeLines/SMA.cp413.v1.0.cppipe @@ -0,0 +1,401 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:ENTROPY_(?P[^.]*)\\.(?P[^.]*).*\\.X(?P[0-9]{1,4}).*\\.Y(?P[0-9]{1,4}) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', '', 'Settings converted from legacy pipeline.', 'Please change any color images from Load as Grayscale image to Load as Color image.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y', 'EntropyImage': 'Y'}, {'Original': 'X', 'EntropyImage': 'X'}, {'Original': 'NR', 'EntropyImage': 'NR'}, {'Original': 'STAIN', 'EntropyImage': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', '', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +ImageMath:[module_num:6|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image:None + Multiply the second image by:1.0 + Measurement: + +UnmixColors:[module_num:7|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find tissue using the grayscale image.', 'White is background, non-white is foreground.', 'Diameter is choosen after some testing.', 'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded areas in contrast to Threshold.', 'From now on, outlined foreground is called Tissue areas (blue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:9|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify Hematoxylin (nuclei) positive areas (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):10,40000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify DAB positive areas (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,40000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:11|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find DAB positive nuclei (red) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +IdentifyPrimaryObjects:[module_num:14|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Find HE positive nuclei (blue) within masked image (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +OverlayOutlines:[module_num:15|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:Blue + Select objects to display:nuclei_HE + Select outline color:Yellow + Select objects to display:DAB_object + Select outline color:Red + Select objects to display:nuclei_HE + Select outline color:Green + Select objects to display:HE_object + Select outline color:black + Select objects to display:Tissue_object + +SaveImages:[module_num:16|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:17|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Binary Image + Select binary images to measure:DAB, DAB_object_mask, HE_object_mask, Hematoxylin, Tissue_object + Select object sets to measure: + +ExportToSpreadsheet:[module_num:18|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SMA_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/SMA.cp413.v1.0.cpproj b/pipeLines/SMA.cp413.v1.0.cpproj new file mode 100644 index 0000000..7a528c4 Binary files /dev/null and b/pipeLines/SMA.cp413.v1.0.cpproj differ diff --git a/pipeLines/_cp220/CD20.AAA.cp220.v2.pipeline.cppipe b/pipeLines/_cp220/CD20.AAA.cp220.v2.pipeline.cppipe new file mode 100644 index 0000000..5cf1f53 --- /dev/null +++ b/pipeLines/_cp220/CD20.AAA.cp220.v2.pipeline.cppipe @@ -0,0 +1,397 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.08,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DABchannel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HEchannel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DABchannel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,30 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Laplacian of Gaussian + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Per object + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:2 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Background + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HEchannel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD20_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD3.AAA.cp220.v2.pipeline.cppipe b/pipeLines/_cp220/CD3.AAA.cp220.v2.pipeline.cppipe new file mode 100644 index 0000000..bbba3da --- /dev/null +++ b/pipeLines/_cp220/CD3.AAA.cp220.v2.pipeline.cppipe @@ -0,0 +1,277 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:16 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:No + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B\x5D + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".overlay") (file does contain ".png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create Grayscale image for finding total tissue area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth grayscale image to remove dust and small open holes\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:OrigGraySmooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert grayscale image for futher analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:Inverted + Image or measurement?:Image + Select the first image:OrigGraySmooth + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Inverted + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):40,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:MoG + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0.3 + Lower and upper bounds on threshold:0.06,1.0 + Approximate fraction of image covered by objects?:0.6 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue areas\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue + +UnmixColors:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Unmix the colors, and extract DAB\', \'Create one grayscale DAB channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Use selected Tissue object to create a mask\', \'Only analyse what\\xe2\\x80\\x99s in the \\xe2\\x80\\x98Tissue\\xe2\\x80\\x99 area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_masked + Use objects or an image as a mask?:Objects + Select object for mask:Tissue + Select image for mask:Tissue + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'What works for CD3 in AAA tissue is\x3A\', \'Otsu Global\', \'Three classes\', \'Entropy\', \'Foreground\', \'Threshold 1.5\', \'Lower 0.7 Upper 1.0\', \'Shape / Shape\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_masked + Name the primary objects to be identified:DAB_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_Nuclei_outline + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure all DAB areas (within Tissue)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:DAB_Nuclei + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The Tissue outline has a thick blue like\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:Orig_Overlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:4 + Select outlines to display:Tissue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The DAB outline is a thin line\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Orig_Overlay + Name the output image:Orig_Overlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:DAB_Nuclei_outline + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + +ExportToSpreadsheet:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CNone + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD3.cp220.v1.1.cppipe b/pipeLines/_cp220/CD3.cp220.v1.1.cppipe new file mode 100755 index 0000000..26084c9 --- /dev/null +++ b/pipeLines/_cp220/CD3.cp220.v1.1.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):10,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD31.cp220.v0.1beta.cppipe b/pipeLines/_cp220/CD31.cp220.v0.1beta.cppipe new file mode 100755 index 0000000..26084c9 --- /dev/null +++ b/pipeLines/_cp220/CD31.cp220.v0.1beta.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):10,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD34.DAB.cp220.v1.2.cppipe b/pipeLines/_cp220/CD34.DAB.cp220.v1.2.cppipe new file mode 100644 index 0000000..b69bff7 --- /dev/null +++ b/pipeLines/_cp220/CD34.DAB.cp220.v1.2.cppipe @@ -0,0 +1,472 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD34.cp220.v1.2.cppipe b/pipeLines/_cp220/CD34.cp220.v1.2.cppipe new file mode 100644 index 0000000..70e150e --- /dev/null +++ b/pipeLines/_cp220/CD34.cp220.v1.2.cppipe @@ -0,0 +1,288 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OriginalGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify tissue (black)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split CD34 positive staining into separate channels (RED\x3A LRP based CD34 staining; DAB for DAB based CD34 staining).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:LPR + Stain:Fast red + Red absorbance:0.8301 + Green absorbance:0.3273 + Blue absorbance:0.2568 + Name the output name:DAB + Stain:Feulgen + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:LPR + Name the primary objects to be identified:LPR_object + Typical diameter of objects, in pixel units (Min,Max):8,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:LPR_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:0.7 + Lower and upper bounds on threshold:0.55,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:LPR_object + Retain a binary image of the object regions?:Yes + Name the output binary image:LPR_nuclei_red + Select a binary image to measure:LPR_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:Tissue_object + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue_object_yellow + Select a binary image to measure:Tissue_object_black + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OriginalGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:LPR_object + Select outlines to display:None + Select outline color:#21FE06 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue_object + Select outlines to display:LPR_object_yellow + Select outline color:#FEFE0A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7CDesktop/CD34/cp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD3_CD56_NKT.cp220.v1.cppipe b/pipeLines/_cp220/CD3_CD56_NKT.cp220.v1.cppipe new file mode 100755 index 0000000..6b8c315 --- /dev/null +++ b/pipeLines/_cp220/CD3_CD56_NKT.cp220.v1.cppipe @@ -0,0 +1,298 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:12 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:No + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:original + Match metadata:\x5B\x5D + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:5 + Select the input color image:original + Name the output name:highdef_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output name:HE + Stain:Custom + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output name:NKT + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output name:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output name:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_TISSUE + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:NKT + Name the primary objects to be identified:NKT_OBJ + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:highdef_yellow + Name the primary objects to be identified:yellow + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:3 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:HE_TISSUE + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:NKT_OBJ + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:yellow + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:highdef_yellow + +OverlayOutlines:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:original + Name the output image:overlay_NKT + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:2 + Select outlines to display:None + Select outline color:#EB6732 + Load outlines from an image or objects?:Objects + Select objects to display:NKT_OBJ + +SaveImages:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:overlay_NKT + Select the objects to save:NKT_OBJ + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder\x7C + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +ExportToSpreadsheet:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CDesktop/NKT images/colorsetting + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Result_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD42B.cp220.v0.1beta.cppipe b/pipeLines/_cp220/CD42B.cp220.v0.1beta.cppipe new file mode 100755 index 0000000..26084c9 --- /dev/null +++ b/pipeLines/_cp220/CD42B.cp220.v0.1beta.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):10,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD66b.cp220.v4.1.cppipe b/pipeLines/_cp220/CD66b.cp220.v4.1.cppipe new file mode 100755 index 0000000..7622a53 --- /dev/null +++ b/pipeLines/_cp220/CD66b.cp220.v4.1.cppipe @@ -0,0 +1,382 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Tissue area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_stain_image + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Nuclei in DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] + Select the input image:DAB_stain_image + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):2,10 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_Nuclei_Red + Select outline color:#FB0009 + Load outlines from an image or objects?:Image + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:3 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_Nuclei_Red + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD68.AAA.cp220.v2.pipeline.cppipe b/pipeLines/_cp220/CD68.AAA.cp220.v2.pipeline.cppipe new file mode 100755 index 0000000..cade566 --- /dev/null +++ b/pipeLines/_cp220/CD68.AAA.cp220.v2.pipeline.cppipe @@ -0,0 +1,397 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DABchannel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HEchannel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DABchannel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HEchannel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD68.cp220.v1.2.cppipe b/pipeLines/_cp220/CD68.cp220.v1.2.cppipe new file mode 100644 index 0000000..38bb4f5 --- /dev/null +++ b/pipeLines/_cp220/CD68.cp220.v1.2.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/CD8.cp220.v0.1beta.cppipe b/pipeLines/_cp220/CD8.cp220.v0.1beta.cppipe new file mode 100755 index 0000000..26084c9 --- /dev/null +++ b/pipeLines/_cp220/CD8.cp220.v0.1beta.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):10,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/EvG.cp220.v1.1.cppipe b/pipeLines/_cp220/EvG.cp220.v1.1.cppipe new file mode 100755 index 0000000..634fd7d --- /dev/null +++ b/pipeLines/_cp220/EvG.cp220.v1.1.cppipe @@ -0,0 +1,304 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:EVG + Stain:Custom + Red absorbance:0.20333 + Green absorbance:0.51696 + Blue absorbance:0.505697 + Name the output name:EVG_IDENTIFYING + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:EVG_IDENTIFYING + Name the primary objects to be identified:EVG_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#107F01 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#FB0106 + Load outlines from an image or objects?:Objects + Select objects to display:EVG_Tissue + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7C + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure total tissue area (green) and total EvG positive stain (red)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:EVG_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:EVG_red + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue_green + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7CDesktop/FIBRINTEST/cp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:EVG_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/FIBRIN.cp220.v1.1.cppipe b/pipeLines/_cp220/FIBRIN.cp220.v1.1.cppipe new file mode 100755 index 0000000..ccd7b17 --- /dev/null +++ b/pipeLines/_cp220/FIBRIN.cp220.v1.1.cppipe @@ -0,0 +1,299 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:FIBRIN + Stain:Custom + Red absorbance:0.345301 + Green absorbance:0.854273 + Blue absorbance:0.388568 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:FIBRIN + Name the primary objects to be identified:Fibrin_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#107F40 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#FB0106 + Load outlines from an image or objects?:Objects + Select objects to display:Fibrin_Tissue + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure total tissue area (green) and total Fibrin positive stain (red)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Fibrin_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:FIBRIN_red + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue_green + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:FIBRIN_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/GLYCC.cp220.v1.2.cppipe b/pipeLines/_cp220/GLYCC.cp220.v1.2.cppipe new file mode 100644 index 0000000..bcf7295 --- /dev/null +++ b/pipeLines/_cp220/GLYCC.cp220.v1.2.cppipe @@ -0,0 +1,260 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:406 +GitHash: +ModuleCount:16 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.', 'Ãĸ\x80\x94', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".bufferexample.png") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.', 'Ãĸ\x80\x94', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\\d*?)\\.(?P(UMC|T.*?))\\.(?P.*?)\\.(?P\\d*?)\\.X(?P\\d+)\\.Y(?P\\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.', 'Ãĸ\x80\x94', 'Settings converted from legacy pipeline.', 'Please change any color images from Ãĸ\x80\x9cLoad as Grayscale imageÃĸ\x80\x9d to Ãĸ\x80\x9cLoad as Color imageÃĸ\x80\x9d']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:[{'Original': 'Y'}, {'Original': 'X'}, {'Original': 'NR'}, {'Original': 'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".bufferexample.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.', 'Ãĸ\x80\x94', 'Settings converted from legacy pipeline.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['Create gray channel, this is used to find tissue (green)', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:False|notes:['Identify Tissue areaÃĸ\x80\x99s (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:11 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Manual threshold:0.05 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +UnmixColors:[module_num:8|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Split HE and DAB into separate channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output image:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:9|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse DAB in the previously defined Tissue']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:10|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse HE in the previously defined Tissue (green)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:'Unknown'|variable_revision_number:14|show_window:False|notes:['Identify DAB positive areaÃĸ\x80\x99s (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:11 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Three classes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Default + +MaskImage:[module_num:12|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:13|svn_version:'Unknown'|variable_revision_number:4|show_window:False|notes:['The next outline is ontop of the previous one.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:Green + Select objects to display:Tissue_object + Select outline color:Yellow + Select objects to display:DAB_object + +SaveImages:[module_num:14|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['Save outlined images', 'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder|None + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:15|svn_version:'Unknown'|variable_revision_number:5|show_window:False|notes:['Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Binary Image + Select binary images to measure:DAB, DAB_object_mask, Hematoxylin, OrigOverlay, Original, OriginalGray, OriginalGray_smooth, Tissue_Masked_DAB_channel, Tissue_Masked_HE_channel + Select object sets to measure: + +ExportToSpreadsheet:[module_num:16|svn_version:'Unknown'|variable_revision_number:13|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Tissue_object|Number_Object_Number,Tissue_object|Location_Center_Y,Tissue_object|Location_Center_X,Image|Count_Tissue_object,Image|Count_DAB_object,Image|AreaOccupied_Perimeter_Tissue_object_green,Image|AreaOccupied_Perimeter_DAB_object_yellow,Image|AreaOccupied_AreaOccupied_Tissue_object_green,Image|AreaOccupied_AreaOccupied_DAB_object_yellow,Image|AreaOccupied_TotalArea_Tissue_object_green,Image|AreaOccupied_TotalArea_DAB_object_yellow,Image|Width_Original,Image|FileName_Original,Image|Height_Original,DAB_object|Location_Center_Y,DAB_object|Location_Center_X,DAB_object|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/HE.cp220.v1.1.cppipe b/pipeLines/_cp220/HE.cp220.v1.1.cppipe new file mode 100755 index 0000000..cb7d17d --- /dev/null +++ b/pipeLines/_cp220/HE.cp220.v1.1.cppipe @@ -0,0 +1,304 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:HE + Stain:Hematoxylin + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + Name the output name:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.27,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:HE_Nuclei + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7C + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:HE_Nuclei + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei_red + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue_green + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:HE_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/SMA.cp220.v3.2.cppipe b/pipeLines/_cp220/SMA.cp220.v3.2.cppipe new file mode 100644 index 0000000..9a64795 --- /dev/null +++ b/pipeLines/_cp220/SMA.cp220.v3.2.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (blue).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):10,40000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,40000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SMA_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/SR.area.cp220.v0.1beta.cp b/pipeLines/_cp220/SR.area.cp220.v0.1beta.cp new file mode 100755 index 0000000..3dc5a75 --- /dev/null +++ b/pipeLines/_cp220/SR.area.cp220.v0.1beta.cp @@ -0,0 +1,234 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'# Load brightfield and polorizes SR slides\', \'(b.g.l.nelissen@umcutrecht.nl)\', \'Goal\x3A Measure collagen and total tissue area (SR_POLARIZED and SR)\', \'We define SR_POLARIZED as blue and SR as green\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:TIF + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:No + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):SR.scaled.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B0-9a-zA-Z\x5D*).(?P\x5B0-9a-zA-Z\x5D*).(?P.*) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:SR + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR\', \'Create Grayscale image for finding total tissue area\'\x5D] + Select the input image:SR + Conversion method:Combine + Image type\x3A:RGB + Name the output image:SR.Gray.INV + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +ImageMath:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Invert grayscale image for futher analysis\'\x5D] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:SR.Gray + Image or measurement?:Image + Select the first image:SR.Gray.INV + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +ApplyThreshold:[module_num:4|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\'# SR\', \'We need to get rid of those black marker dots in the corner of the screen\', \'Therefor we want to mask these areas.\', \'A mask needs to be binary (black/white)\', "To identify the black marker dots we set a threshold to identify all black-dark area\'s"\x5D] + Select the input image:SR.Gray + Name the output image:SR.mask + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Select the thresholding method:Manual + Manual threshold:0.8 + Lower and upper bounds on threshold:0.000000,1.000000 + Threshold correction factor:1 + Approximate fraction of image covered by objects?:0.01 + Select the input objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Select the measurement to threshold with:None + +Smooth:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'# SR\', \'We need to smooth the previous identified black marker dots at bit to fill small holes\'\x5D] + Select the input image:SR.mask + Name the output image:SR.mask + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:30 + Edge intensity difference:0.1 + +ApplyThreshold:[module_num:6|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\'# SR\', \'The smoothed mask needs to become binary again.\'\x5D] + Select the input image:SR.mask + Name the output image:SR.mask + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Select the thresholding method:Manual + Manual threshold:0.2 + Lower and upper bounds on threshold:0.000000,1.000000 + Threshold correction factor:1 + Approximate fraction of image covered by objects?:0.01 + Select the input objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Select the measurement to threshold with:None + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'The mask is now created, we only need to invert it (black = white and white = black)\', \'\'\x5D] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:SR.mask + Image or measurement?:Image + Select the first image:SR.mask + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Here we apply the mask and use it to remove the black marker dots\'\x5D] + Select the input image:SR.Gray + Name the output image:SR.Gray.masked + Use objects or an image as a mask?:Image + Select object for mask:None + Select image for mask:SR.mask + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'# SR\', \'Find tissue using the grayscale image.\', \'Diameter is choosen after some testing.\', \'These settings are choosen after multiple tests\'\x5D] + Select the input image:SR.Gray.masked + Name the primary objects to be identified:SR.TISSUE.green + Typical diameter of objects, in pixel units (Min,Max):10,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.6 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:SR.TISSUE.green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR\', \'Show what we defined as objects using a green outline\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:SR + Name the output image:SR.Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:SR.TISSUE.green + Select outline color:Green + +SaveImages:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR\', \'Save a copy of SR including measurements outlines in green.\'\x5D] + Select the type of image to save:Image + Select the image to save:SR.Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:SR + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.cellprofiler + Select file format to use:png + Output file location:Default Output Folder sub-folder\x7Coutline + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Store file and path information to the saved image?:Yes + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Measure area in pixels (green and blue)\', \'Both measurements are done using the same scaled image and can there be used to calculate a ratio.\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR.TISSUE.green + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR\', "A folder \'SR\' is created in the outputfolder and contains \'Image.csv\' with your measurements", "Using \'Press to select measurements\' you can choose to export additional measurements", "Using \'Data to export\' you can choose to export addition datasets"\x5D] + Select or enter the column delimiter:Comma (",") + Prepend the output file name to the data file names?:Yes + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the columns of measurements to export?:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7CSR + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurements, using default file names?:No + Press button to select measurements to export:Image\x7CAreaOccupied_AreaOccupied_SR.TISSUE.green,Image\x7CFileName_SR,Image\x7CPathName_SR,Image\x7CMetadata_STUDYNR + Data to export:Image + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/SR.cp220.v1.1.cppipe b/pipeLines/_cp220/SR.cp220.v1.1.cppipe new file mode 100755 index 0000000..178b8d5 --- /dev/null +++ b/pipeLines/_cp220/SR.cp220.v1.1.cppipe @@ -0,0 +1,275 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:SR + Stain:Custom + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:SR + Name the primary objects to be identified:SR_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,100 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.25,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:SR_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder sub-folder\x7Ccp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SR_ + Overwrite existing files without warning?:No + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_cp220/SR.cp220.v1.cppipe b/pipeLines/_cp220/SR.cp220.v1.cppipe new file mode 100755 index 0000000..c21ff45 --- /dev/null +++ b/pipeLines/_cp220/SR.cp220.v1.cppipe @@ -0,0 +1,276 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:SR + Stain:Custom + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:SR + Name the primary objects to be identified:SR_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,100 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.25,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:SR_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7Ccp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:No + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/_cp220/SR_POLARIZED.area.cp220.v0.1beta.cp b/pipeLines/_cp220/SR_POLARIZED.area.cp220.v0.1beta.cp new file mode 100755 index 0000000..786dc7a --- /dev/null +++ b/pipeLines/_cp220/SR_POLARIZED.area.cp220.v0.1beta.cp @@ -0,0 +1,164 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'# Load polorizes SR slides\', \'(b.g.l.nelissen@umcutrecht.nl)\', \'Goal\x3A Measure polarized collagen area (SR_POLARIZED)\', \'We define SR_POLARIZED as blue\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:TIF + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:No + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):SR_POLARIZED.scaled.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B0-9a-zA-Z\x5D*).(?P\x5B0-9a-zA-Z\x5D*).(?P.*) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:SR_POLARIZED + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +RescaleIntensity:[module_num:2|svn_version:\'6746\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Rescale intensity to re-level the brightness of each image and make further analysis more uniform.\', \'\'\x5D] + Select the input image:SR_POLARIZED + Name the output image:Original + Select rescaling method:Divide by the image\'s maximum + How do you want to calculate the minimum intensity?:Custom + How do you want to calculate the maximum intensity?:Custom + Enter the lower limit for the intensity range for the input image:0 + Enter the upper limit for the intensity range for the input image:1 + Enter the intensity range for the input image:0.000000,1.000000 + Enter the desired intensity range for the final, rescaled image:0.000000,1.000000 + Select method for rescaling pixels below the lower limit:Mask pixels + Enter custom value for pixels below lower limit:0 + Select method for rescaling pixels above the upper limit:Mask pixels + Enter custom value for pixels below upper limit:0 + Select image to match in maximum intensity:None + Enter the divisor:1 + Select the measurement to use as a divisor:None + +ColorToGray:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Create Grayscale image for finding total tissue area\', \'\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:SR_POLARIZED.Gray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Green\x3A 2 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +IdentifyPrimaryObjects:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'# SR_POLARIZED\', \'Find tissue using the grayscale image.\', \'Diameter is choosen after some testing.\', \'These settings are choosen after multiple tests\'\x5D] + Select the input image:SR_POLARIZED.Gray + Name the primary objects to be identified:SR_POLARIZED.TISSUE.blue + Typical diameter of objects, in pixel units (Min,Max):10,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0,1.0 + Approximate fraction of image covered by objects?:0.6 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:SR_POLARIZED.TISSUE.blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.20 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:Scaling_Original + +OverlayOutlines:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Show what we defined as objects using a blue outline\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:SR_POLARIZED + Name the output image:SR_POLARIZED.Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:SR_POLARIZED.TISSUE.blue + Select outline color:Blue + +SaveImages:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Save a copy of SR_POLARIZED including measurements outlines in blue.\'\x5D] + Select the type of image to save:Image + Select the image to save:SR_POLARIZED.Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:SR_POLARIZED + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.cellprofiler + Select file format to use:png + Output file location:Default Output Folder sub-folder\x7Coutline + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Store file and path information to the saved image?:Yes + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR_POLARIZED & SR\', \'Measure area in pixels (green and blue)\', \'Both measurements are done using the same scaled image and can there be used to calculate a ratio.\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR_POLARIZED.TISSUE.blue + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR_POLARIZED\', "A folder \'SR_Polarized\' is created in the outputfolder and contains \'Image.csv\' with your measurements", "Using \'Press to select measurements\' you can choose to export additional measurements", "Using \'Data to export\' you can choose to export addition datasets"\x5D] + Select or enter the column delimiter:Comma (",") + Prepend the output file name to the data file names?:Yes + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the columns of measurements to export?:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7CSR_POLARIZED + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurements, using default file names?:No + Press button to select measurements to export:Image\x7CAreaOccupied_AreaOccupied_SR_POLARIZED.TISSUE.blue,Image\x7CFileName_SR_POLARIZED,Image\x7CPathName_SR_POLARIZED,Image\x7CMetadata_STUDYNR + Data to export:Image + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD20.AAA.cp220.v1.pipeline.cp b/pipeLines/_legacy_archived/CD20.AAA.cp220.v1.pipeline.cp new file mode 100755 index 0000000..1ffbcbf --- /dev/null +++ b/pipeLines/_legacy_archived/CD20.AAA.cp220.v1.pipeline.cp @@ -0,0 +1,345 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.08,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,30 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu PerObject + Threshold correction factor:2 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Laplacian of Gaussian + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Background + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:Tissue_Masked_HE-channel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToDatabase:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:20|show_window:True|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD3.AAA.cp220.v1.pipeline.cp b/pipeLines/_legacy_archived/CD3.AAA.cp220.v1.pipeline.cp new file mode 100755 index 0000000..d8389bc --- /dev/null +++ b/pipeLines/_legacy_archived/CD3.AAA.cp220.v1.pipeline.cp @@ -0,0 +1,284 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'# Pipeline created for HPC (b.g.l.nelissen@umcutrecht.nl)\', \'# Goal\x3A Measure total tissue area, DAB area and DAB positive cells\', \'# Outlines\x3A Tissue blue, DAB nuclei green\', \'\', \'# Testing oneliner\x3A find ./ -name "*tile-*" -not -path "*out*" -type d -exec "$(command -v "slideJobsCellProfiler")" -i "{}" -p "pipeline.cp" \\\\; \x7C parallel\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.overlay + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.png + Position of this image in each group:1 + Extract metadata from where?:None + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:No + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create Grayscale image for finding total tissue area\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:RGB + Name the output image:OrigGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Smooth:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth grayscale image to remove dust and small open holes\'\x5D] + Select the input image:OrigGray + Name the output image:OrigGraySmooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:20 + Edge intensity difference:0.1 + +ImageMath:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Invert grayscale image for futher analysis\'\x5D] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:Inverted + Image or measurement?:Image + Select the first image:OrigGraySmooth + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', "Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\'s in contrast to Threshold.", \'From now on, outlined foreground is called Tissue\'\x5D] + Select the input image:Inverted + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):40,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:MoG Global + Threshold correction factor:0.3 + Lower and upper bounds on threshold:0.06,1.0 + Approximate fraction of image covered by objects?:0.6 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MeasureImageAreaOccupied:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue areas\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Unmix the colors, and extract DAB\', \'Create one grayscale DAB channel\'\x5D] + Stain count:1 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Use selected Tissue object to create a mask\', "Only analyse what\'s in the \'Tissue\' area"\x5D] + Select the input image:DAB + Name the output image:DAB_masked + Use objects or an image as a mask?:Objects + Select object for mask:Tissue + Select image for mask:Tissue + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'What works for CD3 in AAA tissue is\x3A\', \'Otsu Global\', \'Three classes\', \'Entropy\', \'Foreground\', \'Threshold 1.5\', \'Lower 0.7 Upper 1.0\', \'Shape / Shape\'\x5D] + Select the input image:DAB_masked + Name the primary objects to be identified:DAB_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_Nuclei_outline + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MeasureImageAreaOccupied:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure all DAB areas (within Tissue)\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:DAB_Nuclei + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The Tissue outline has a thick blue like\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:Orig_Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:4 + Select outlines to display:Tissue + Select outline color:Blue + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The DAB outline is a thin line\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Orig_Overlay + Name the output image:Orig_Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:DAB_Nuclei_outline + Select outline color:Green + +SaveImages:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D] + Select the type of image to save:Image + Select the image to save:Orig_Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.outlines + Select file format to use:jpg + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\x5D] + Database type:MySQL / CSV + Database name:DefaultDB + Add a prefix to table names?:No + Table prefix:Expt_ + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:Yes + Database host: + Username: + Password: + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:No + Calculate the per-image median values of object measurements?:No + Calculate the per-image standard deviation values of object measurements?:No + Calculate the per-well mean values of object measurements?:No + Calculate the per-well median values of object measurements?:No + Calculate the per-well standard deviation values of object measurements?:No + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:Yes + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:Yes + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No + +ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D] + Select or enter the column delimiter:Comma (",") + Prepend the output file name to the data file names?:Yes + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the columns of measurements to export?:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CNone + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurements, using default file names?:Yes + Press button to select measurements to export: + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD3.V1.pipeline.cppipe b/pipeLines/_legacy_archived/CD3.V1.pipeline.cppipe new file mode 100755 index 0000000..3b8d98b --- /dev/null +++ b/pipeLines/_legacy_archived/CD3.V1.pipeline.cppipe @@ -0,0 +1,301 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from image file headers + Metadata source:File name + Regular expression:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D)_w(?P\x5B0-9\x5D) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B\x5D + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".overlay") (file does contain ".png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create Grayscale image for finding total tissue area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth grayscale image to remove dust and small open holes\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:OrigGraySmooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert grayscale image for futher analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:Inverted + Image or measurement?:Image + Select the first image:OrigGraySmooth + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Inverted + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):40,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:MoG + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0.3 + Lower and upper bounds on threshold:0.06,1.0 + Approximate fraction of image covered by objects?:0.6 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue areas\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue + +UnmixColors:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Unmix the colors, and extract DAB\', \'Create one grayscale DAB channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Use selected Tissue object to create a mask\', \'Only analyse what\\xe2\\x80\\x99s in the \\xe2\\x80\\x98Tissue\\xe2\\x80\\x99 area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_masked + Use objects or an image as a mask?:Objects + Select object for mask:Tissue + Select image for mask:Tissue + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'What works for CD3 in AAA tissue is\x3A\', \'Otsu Global\', \'Three classes\', \'Entropy\', \'Foreground\', \'Threshold 1.5\', \'Lower 0.7 Upper 1.0\', \'Shape / Shape\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_masked + Name the primary objects to be identified:DAB_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,26 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_Nuclei_outline + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.5 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure all DAB areas (within Tissue)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:DAB_Nuclei + Retain a binary image of the object regions?:Yes + Name the output binary image:Stain_DAB_Nuclei + Select a binary image to measure:Tissue + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The Tissue outline has a thick blue line.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:Orig_Overlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:4 + Select outlines to display:Tissue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create outlines for visualisation.\', \'The DAB outline is a thin line.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Orig_Overlay + Name the output image:Orig_Overlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:DAB_Nuclei_outline + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:Orig_Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.outlines + Saved file format:jpg + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C/Users/swvanderlaan/PLINK/analyses/expressscan + Saved movie format:avi + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Input Folder sub-folder\x7Cexpressscan/CD3/_ANALYSES + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD34.V1.cppipe b/pipeLines/_legacy_archived/CD34.V1.cppipe new file mode 100755 index 0000000..957d242 --- /dev/null +++ b/pipeLines/_legacy_archived/CD34.V1.cppipe @@ -0,0 +1,275 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:RED + Stain:Fast red + Red absorbance:0.8301 + Green absorbance:0.3273 + Blue absorbance:0.2568 + Name the output name:DAB + Stain:Feulgen + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:RED + Name the primary objects to be identified:RED_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:0.7 + Lower and upper bounds on threshold:0.55,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:RED_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:RED_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CDesktop/CD34/cp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD34.area.under_construction.cp b/pipeLines/_legacy_archived/CD34.area.under_construction.cp new file mode 100755 index 0000000..9d033ca --- /dev/null +++ b/pipeLines/_legacy_archived/CD34.area.under_construction.cp @@ -0,0 +1,212 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +UnmixColors:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and Liquid Permanent Red into separate channels\'\x5D] + Stain count:3 + Color image:Original + Image name:Liquid Permanent Red + Stain:Custom + Red absorbance:0.0684437 + Green absorbance:0.615381 + Blue absorbance:0.785253 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + Image name:DAB + Stain:DAB + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify LPR positive area\'s (yellow)"\x5D] + Select the input image:Liquid Permanent Red + Name the primary objects to be identified:LPRed_object + Typical diameter of objects, in pixel units (Min,Max):8,4000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.20,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:LPR_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the output image:LPR_Masked + Use objects or an image as a mask?:Objects + Select object for mask:LPRed_object + Select image for mask:None + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:LPR_Masked + Name the primary objects to be identified:Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.35,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Nuclei_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:LPR_object_yellow + Select outline color:Yellow + Select outlines to display:Nuclei_object_green + Select outline color:Green + +SaveImages:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:LPR_object_yellow + +ExportToDatabase:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD34.cp220.v1.1.cppipe b/pipeLines/_legacy_archived/CD34.cp220.v1.1.cppipe new file mode 100755 index 0000000..d7428fa --- /dev/null +++ b/pipeLines/_legacy_archived/CD34.cp220.v1.1.cppipe @@ -0,0 +1,283 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OriginalGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OriginalGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify tissue (black)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split CD34 positive staining into separate channels (RED\x3A LRP based CD34 staining; DAB for DAB based CD34 staining).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:RED + Stain:Fast red + Red absorbance:0.8301 + Green absorbance:0.3273 + Blue absorbance:0.2568 + Name the output name:DAB + Stain:Feulgen + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:RED + Name the primary objects to be identified:RED_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:0.7 + Lower and upper bounds on threshold:0.55,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:RED_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OriginalGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:RED_Tissue + Select outlines to display:nuclei_red + Select outline color:#FEFE0A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7CDesktop/CD34/cp_output + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD34_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD34.lpr.entropy.cp b/pipeLines/_legacy_archived/CD34.lpr.entropy.cp new file mode 100755 index 0000000..1b73b1b --- /dev/null +++ b/pipeLines/_legacy_archived/CD34.lpr.entropy.cp @@ -0,0 +1,166 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:Do not use + Analyze all subfolders within the selected folder?:None + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:No + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:3 + Text that these images have in common (case-sensitive):ENTROPY_ + Position of this image in each group:1 + Extract metadata from where?:None + Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:TissueMaskImage + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:LoadedImageOutlines + Channel number:1 + Rescale intensities?:Yes + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:2 + Extract metadata from where?:None + Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:OriginalImage + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:LoadedImageOutlines + Channel number:1 + Rescale intensities?:Yes + Text that these images have in common (case-sensitive):LPR_ + Position of this image in each group:3 + Extract metadata from where?:None + Regular expression that finds metadata in the file name:^(?P.*)_(?P\x5BA-P\x5D\x5B0-9\x5D{2})_s(?P\x5B0-9\x5D) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:LPR_Image + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:LoadedImageOutlines + Channel number:1 + Rescale intensities?:Yes + +IdentifyPrimaryObjects:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:TissueMaskImage + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):150,4000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:TisueOutlines + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:TissueBinary + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:LPR_Image + Name the primary objects to be identified:LPR_Area + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:LPR_Area_Outlines + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:LPRBinary + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:OriginalImage + Name the output image:OverlayResults + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:2 + Select outlines to display:TisueOutlines + Select outline color:Green + Select outlines to display:LPR_Area_Outlines + Select outline color:Red + +SaveImages:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D] + Select the type of image to save:Image + Select the image to save:OverlayResults + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:OriginalImage + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No diff --git a/pipeLines/_legacy_archived/CD34.under_construction.cp b/pipeLines/_legacy_archived/CD34.under_construction.cp new file mode 100755 index 0000000..709b5da --- /dev/null +++ b/pipeLines/_legacy_archived/CD34.under_construction.cp @@ -0,0 +1,329 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +UnmixColors:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:LPR + Stain:Custom + Red absorbance:0.0320406 + Green absorbance:0.970037 + Blue absorbance:0.240834 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:LPR + Name the primary objects to be identified:LPR_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:LPR_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:LPR + Name the output image:LPR_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:LPR_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:LPR_object_mask + Name the primary objects to be identified:nuclei_LPR + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_LPR_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:LPR_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_LPR_red + Select outline color:Red + Select outlines to display:HE_object_green + Select outline color:Green + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:LPR_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_LPR_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.1.cppipe b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.1.cppipe new file mode 100644 index 0000000..4edb32c --- /dev/null +++ b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.1.cppipe @@ -0,0 +1,255 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:12 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:No + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:original + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does contain "") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:5 + Select the input color image:original + Name the output image:highdef_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output image:HE + Stain:Custom + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output image:NKT + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output image:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output image:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_TISSUE + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:NKT + Name the primary objects to be identified:NKT_OBJ + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:highdef_yellow + Name the primary objects to be identified:yellow + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +MeasureImageAreaOccupied:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:NKT_OBJ, yellow, HE_TISSUE + +OverlayOutlines:[module_num:10|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:original + Name the output image:overlay_NKT + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#EB6732 + Select objects to display:NKT_OBJ + +SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:overlay_NKT + Select method for constructing file names:From image filename + Select image name for file prefix:original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +ExportToSpreadsheet:[module_num:12|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder|Desktop/NKT images/colorsetting + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_CD56_NKT_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.2.cppipe b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.2.cppipe new file mode 100644 index 0000000..350ab51 --- /dev/null +++ b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.2.cppipe @@ -0,0 +1,255 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:12 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:No + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:original + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does contain "") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:5 + Select the input color image:original + Name the output image:highdef_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output image:HE + Stain:Custom + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output image:NKT + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output image:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output image:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_TISSUE + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:NKT + Name the primary objects to be identified:NKT_OBJ + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:highdef_yellow + Name the primary objects to be identified:yellow + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +MeasureImageAreaOccupied:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:NKT_OBJ, yellow, HE_TISSUE + +OverlayOutlines:[module_num:10|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:original + Name the output image:overlay_NKT + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#EB6732 + Select objects to display:NKT_OBJ + +SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:overlay_NKT + Select method for constructing file names:From image filename + Select image name for file prefix:original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +ExportToSpreadsheet:[module_num:12|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder|Desktop/NKT images/colorsetting + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_original + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD3_CD56_NKT_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.cppipe b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.cppipe new file mode 100644 index 0000000..8cf6630 --- /dev/null +++ b/pipeLines/_legacy_archived/CD3_CD56_NKT.cp413.v1.cppipe @@ -0,0 +1,255 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:12 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Images only + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\\\\\/]\\\\.") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:No + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?P.*)_(?P[A-P][0-9]{2})_s(?P[0-9])_w(?P[0-9]) + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:All images + Select the filtering criteria:and (file does contain "") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:original + Match metadata:[] + Image set matching method:Order + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file does contain "") + Name to assign these images:DNA + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:5 + Select the input color image:original + Name the output image:highdef_yellow + Stain:Custom + Red absorbance:0.480306 + Green absorbance:0.560244 + Blue absorbance:0.674857 + Name the output image:HE + Stain:Custom + Red absorbance:0.526374 + Green absorbance:0.666769 + Blue absorbance:0.527589 + Name the output image:NKT + Stain:Custom + Red absorbance:0.459621 + Green absorbance:0.570515 + Blue absorbance:0.680633 + Name the output image:background_grey + Stain:Custom + Red absorbance:0.590032 + Green absorbance:0.629084 + Blue absorbance:0.506078 + Name the output image:background_tissue + Stain:Fast blue + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_TISSUE + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.45,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:NKT + Name the primary objects to be identified:NKT_OBJ + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.5,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:highdef_yellow + Name the primary objects to be identified:yellow + Typical diameter of objects, in pixel units (Min,Max):1,4000 + Discard objects outside the diameter range?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +MeasureImageAreaOccupied:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure: + Select object sets to measure:NKT_OBJ, yellow, HE_TISSUE + +OverlayOutlines:[module_num:10|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:original + Name the output image:overlay_NKT + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#EB6732 + Select objects to display:NKT_OBJ + +SaveImages:[module_num:11|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:overlay_NKT + Select method for constructing file names:From image filename + Select image name for file prefix:original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:OVERLAY_ + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +ExportToSpreadsheet:[module_num:12|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder|Desktop/NKT images/colorsetting + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + Press button to select measurements: + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Result_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b.V1.cppipe b/pipeLines/_legacy_archived/CD66b.V1.cppipe new file mode 100755 index 0000000..e88ded0 --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b.V1.cppipe @@ -0,0 +1,326 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,500 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b.V2.cppipe b/pipeLines/_legacy_archived/CD66b.V2.cppipe new file mode 100755 index 0000000..de2717a --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b.V2.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_Masked_Tissue_channel + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_Masked_Tissue_channel + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):10,15 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b.V3.cppipe b/pipeLines/_legacy_archived/CD66b.V3.cppipe new file mode 100755 index 0000000..c6acd7a --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b.V3.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.45,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_Masked_Tissue_channel + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_Masked_Tissue_channel + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):10,15 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b.V4.1.pipeline.cppipe b/pipeLines/_legacy_archived/CD66b.V4.1.pipeline.cppipe new file mode 100755 index 0000000..671dc2d --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b.V4.1.pipeline.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression: + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth grayscale image to remove dust and small open holes using a Gaussion Filter. This is used to find Tissue (green).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (green).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Unmix the colors, split HE and DAB into separate channels, and extract DAB.\', \'Create one grayscale DAB channel.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Use selected Tissue object to create a mask. Only analyse DAB in the previously defined Tissue area.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue area.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_stain_image + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Nuclei in DAB positive area\\xe2\\x80\\x99s (yellow).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] + Select the input image:DAB_stain_image + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):2,10 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b.V4.cppipe b/pipeLines/_legacy_archived/CD66b.V4.cppipe new file mode 100755 index 0000000..494f426 --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b.V4.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_stain_image + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] + Select the input image:DAB_stain_image + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):2,10 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC.cppipe b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC.cppipe new file mode 100755 index 0000000..e88ded0 --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC.cppipe @@ -0,0 +1,326 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,500 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V2.cppipe b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V2.cppipe new file mode 100755 index 0000000..de2717a --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V2.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_Masked_Tissue_channel + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_Masked_Tissue_channel + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):10,15 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V3.cppipe b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V3.cppipe new file mode 100755 index 0000000..c6acd7a --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V3.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.45,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_Masked_Tissue_channel + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_Masked_Tissue_channel + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):10,15 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V4.cppipe b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V4.cppipe new file mode 100755 index 0000000..494f426 --- /dev/null +++ b/pipeLines/_legacy_archived/CD66b_SPREADSHEET.HPC_V4.cppipe @@ -0,0 +1,377 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_HE_channel + Name the output image:DAB_stain_image + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Nuclei in DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] + Select the input image:DAB_stain_image + Name the primary objects to be identified:DAB_Nuclei_object + Typical diameter of objects, in pixel units (Min,Max):2,10 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_Nuclei_Red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.7,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:None + Select outline color:#FB0009 + Load outlines from an image or objects?:Objects + Select objects to display:DAB_Nuclei_object + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD66b_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD68.AAA.cp220.v1.pipeline.cp b/pipeLines/_legacy_archived/CD68.AAA.cp220.v1.pipeline.cp new file mode 100755 index 0000000..54133fd --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.AAA.cp220.v1.pipeline.cp @@ -0,0 +1,345 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:Tissue_Masked_HE-channel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToDatabase:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:20|show_window:True|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD68.V0.cp b/pipeLines/_legacy_archived/CD68.V0.cp new file mode 100755 index 0000000..b9c4f62 --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.V0.cp @@ -0,0 +1,329 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +UnmixColors:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_Object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:DAB + Name the primary objects to be identified:DAB_Object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_Object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_Object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,50 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:HE_object_green + Select outline color:Green + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD68.V1.cppipe b/pipeLines/_legacy_archived/CD68.V1.cppipe new file mode 100755 index 0000000..49101cf --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.V1.cppipe @@ -0,0 +1,381 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:15 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +ApplyThreshold:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define HE positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Area_Hematoxyin + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +ApplyThreshold:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define DAB positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Area_DAB + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure DAB and HE area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_Hematoxyin + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_DAB + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:object_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create a mask to identify tissue inwhich we count DAB positive objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find DAB positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB_only + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:nuclei_hematoxylin + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_all + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Overlay outlines of identified objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:object_DAB + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_all + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:No + :Image\x7CCount_DAB_object,Image\x7CWidth_Original,Image\x7CHeight_Original,Image\x7CFileName_Original,nuclei_DAB_only\x7CLocation_Center_Y,nuclei_DAB_only\x7CLocation_Center_X,nuclei_DAB_only\x7CNumber_Object_Number,DAB_object\x7CNumber_Object_Number,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:DAB_object + Combine these object measurements with those of the previous object?:No + File name:DAB_OBJECT.txt + Use the object name for the file name?:No + Data to export:nuclei_DAB_only + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_DAB.txt + Use the object name for the file name?:No + Data to export:nuclei_hematoxylin + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_HEMATOXYLIN.txt + Use the object name for the file name?:No diff --git a/pipeLines/_legacy_archived/CD68.area.V0.cp b/pipeLines/_legacy_archived/CD68.area.V0.cp new file mode 100755 index 0000000..d6f7d71 --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.area.V0.cp @@ -0,0 +1,255 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'11587\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'10318\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'10465\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'10829\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.075,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:10|svn_version:\'10672\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + +SaveImages:[module_num:11|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:12|svn_version:\'10563\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToDatabase:[module_num:13|svn_version:\'10962\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD68.area.V1.cp b/pipeLines/_legacy_archived/CD68.area.V1.cp new file mode 100755 index 0000000..54133fd --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.area.V1.cp @@ -0,0 +1,345 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:Tissue_Masked_HE-channel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToDatabase:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:20|show_window:True|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/CD68.cp220.v1.1.cppipe b/pipeLines/_legacy_archived/CD68.cp220.v1.1.cppipe new file mode 100755 index 0000000..39d484e --- /dev/null +++ b/pipeLines/_legacy_archived/CD68.cp220.v1.1.cppipe @@ -0,0 +1,472 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD68_MERGE_V0.9.cppipe b/pipeLines/_legacy_archived/CD68_MERGE_V0.9.cppipe new file mode 100755 index 0000000..a299667 --- /dev/null +++ b/pipeLines/_legacy_archived/CD68_MERGE_V0.9.cppipe @@ -0,0 +1,373 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:15 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +ApplyThreshold:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define HE positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Area_Hematoxyin + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +ApplyThreshold:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define DAB positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Area_DAB + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure DAB and HE area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_Hematoxyin + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_DAB + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:object_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create a mask to identify tissue inwhich we count DAB positive objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find DAB positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB_only + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:nuclei_hematoxylin + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_all + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Overlay outlines of identified objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:object_DAB + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_all + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Image\x7CCount_DAB_object,Image\x7CWidth_Original,Image\x7CHeight_Original,Image\x7CFileName_Original,nuclei_DAB_only\x7CLocation_Center_Y,nuclei_DAB_only\x7CLocation_Center_X,nuclei_DAB_only\x7CNumber_Object_Number,DAB_object\x7CNumber_Object_Number,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/CD68_MERGE_V1.cppipe b/pipeLines/_legacy_archived/CD68_MERGE_V1.cppipe new file mode 100755 index 0000000..49101cf --- /dev/null +++ b/pipeLines/_legacy_archived/CD68_MERGE_V1.cppipe @@ -0,0 +1,381 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:15 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +ApplyThreshold:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define HE positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Area_Hematoxyin + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +ApplyThreshold:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define DAB positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Area_DAB + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure DAB and HE area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_Hematoxyin + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_DAB + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:object_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create a mask to identify tissue inwhich we count DAB positive objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find DAB positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB_only + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:nuclei_hematoxylin + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_all + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Overlay outlines of identified objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:object_DAB + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_all + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:No + :Image\x7CCount_DAB_object,Image\x7CWidth_Original,Image\x7CHeight_Original,Image\x7CFileName_Original,nuclei_DAB_only\x7CLocation_Center_Y,nuclei_DAB_only\x7CLocation_Center_X,nuclei_DAB_only\x7CNumber_Object_Number,DAB_object\x7CNumber_Object_Number,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:DAB_object + Combine these object measurements with those of the previous object?:No + File name:DAB_OBJECT.txt + Use the object name for the file name?:No + Data to export:nuclei_DAB_only + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_DAB.txt + Use the object name for the file name?:No + Data to export:nuclei_hematoxylin + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_HEMATOXYLIN.txt + Use the object name for the file name?:No diff --git a/pipeLines/_legacy_archived/CD68_V1.cppipe b/pipeLines/_legacy_archived/CD68_V1.cppipe new file mode 100755 index 0000000..49101cf --- /dev/null +++ b/pipeLines/_legacy_archived/CD68_V1.cppipe @@ -0,0 +1,381 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:15 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +UnmixColors:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +ApplyThreshold:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define HE positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Area_Hematoxyin + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +ApplyThreshold:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Define DAB positive area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Area_DAB + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure DAB and HE area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_Hematoxyin + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Area_DAB + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):9,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:object_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create a mask to identify tissue inwhich we count DAB positive objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find DAB positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB_only + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:nuclei_hematoxylin + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_all + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Overlay outlines of identified objects\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:object_DAB + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_all + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +ExportToSpreadsheet:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:No + :Image\x7CCount_DAB_object,Image\x7CWidth_Original,Image\x7CHeight_Original,Image\x7CFileName_Original,nuclei_DAB_only\x7CLocation_Center_Y,nuclei_DAB_only\x7CLocation_Center_X,nuclei_DAB_only\x7CNumber_Object_Number,DAB_object\x7CNumber_Object_Number,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:CD68_ + Overwrite existing files without warning?:Yes + Data to export:DAB_object + Combine these object measurements with those of the previous object?:No + File name:DAB_OBJECT.txt + Use the object name for the file name?:No + Data to export:nuclei_DAB_only + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_DAB.txt + Use the object name for the file name?:No + Data to export:nuclei_hematoxylin + Combine these object measurements with those of the previous object?:No + File name:NUCLEI_HEMATOXYLIN.txt + Use the object name for the file name?:No diff --git a/pipeLines/_legacy_archived/EVG.area.V1.cp b/pipeLines/_legacy_archived/EVG.area.V1.cp new file mode 100755 index 0000000..f446792 --- /dev/null +++ b/pipeLines/_legacy_archived/EVG.area.V1.cp @@ -0,0 +1,247 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. EVG area (blue)\', \'\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:EVG + Stain:Hematoxylin + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:DAB + Stain:DAB + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:EVG + Name the output image:Tissue_Masked_EVG-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:Tissue_Masked_EVG-channel + Name the primary objects to be identified:EVG + Typical diameter of objects, in pixel units (Min,Max):25,4000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.10,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:EVG_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:EVG_blue + Select outline color:Blue + +SaveImages:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:EVG_blue + +ExportToDatabase:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/EVG.area.V1.cppipe b/pipeLines/_legacy_archived/EVG.area.V1.cppipe new file mode 100755 index 0000000..9f73f33 --- /dev/null +++ b/pipeLines/_legacy_archived/EVG.area.V1.cppipe @@ -0,0 +1,330 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:15 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:EVG + Stain:Hematoxylin + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:DAB + Stain:DAB + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:EVG + Name the output image:Tissue_Masked_EVG_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_EVG_channel + Name the primary objects to be identified:EVG + Typical diameter of objects, in pixel units (Min,Max):25,4000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:EVG_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.10,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:EVG_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C/Users/swvanderlaan/PLINK/analyses/expressscan/expressscan + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:EVG_blue + +ExportToDatabase:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:27|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object, a single object table or a single object view?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images for which you want to save thumbnails: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Properties image group count:1 + Properties group field count:1 + Properties filter field count:0 + Workspace measurement count:1 + Experiment name:MyExpt + Which objects should be used for locations?:None + Enter a phenotype class table name if using the classifier tool: + Export object relationships?:No + Overwrite without warning?:Data only + Access CPA images via URL?:No + Select the classification type:Object + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No + Create a CellProfiler Analyst workspace file?:No + Select the measurement display tool:ScatterPlot + Type of measurement to plot on the X-axis:Image + Enter the object name:Image + Select the X-axis measurement: + Select the X-axis index:ImageNumber + Type of measurement to plot on the Y-axis:Image + Enter the object name:Image + Select the Y-axis measurement: + Select the Y-axis index:ImageNumber diff --git a/pipeLines/_legacy_archived/EvG.cp220.v1.0.1.cppipe b/pipeLines/_legacy_archived/EvG.cp220.v1.0.1.cppipe new file mode 100755 index 0000000..88018ae --- /dev/null +++ b/pipeLines/_legacy_archived/EvG.cp220.v1.0.1.cppipe @@ -0,0 +1,280 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:EVG + Stain:Custom + Red absorbance:0.20333 + Green absorbance:0.51696 + Blue absorbance:0.505697 + Name the output name:EVG_IDENTIFYING + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:EVG_IDENTIFYING + Name the primary objects to be identified:EVG_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:EVG_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:EVG_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CDesktop/FIBRINTEST/cp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/EvG.cp220.v1.cppipe b/pipeLines/_legacy_archived/EvG.cp220.v1.cppipe new file mode 100755 index 0000000..bd45033 --- /dev/null +++ b/pipeLines/_legacy_archived/EvG.cp220.v1.cppipe @@ -0,0 +1,276 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:EVG + Stain:Custom + Red absorbance:0.20333 + Green absorbance:0.51696 + Blue absorbance:0.505697 + Name the output name:EVG_IDENTIFYING + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:EVG_IDENTIFYING + Name the primary objects to be identified:EVG_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:.8 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:EVG_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:EVG_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7CDesktop/FIBRINTEST/cp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/_legacy_archived/FIBRIN.area.V1.cp b/pipeLines/_legacy_archived/FIBRIN.area.V1.cp new file mode 100755 index 0000000..bc670ba --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN.area.V1.cp @@ -0,0 +1,254 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:None + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:2 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + Text that these images have in common (case-sensitive):tile.tissue.bmp + Position of this image in each group:2 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:ENTROPY_(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:entropy_image + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:LoadedImageOutlines + Channel number:1 + Rescale intensities?:Yes + +ApplyThreshold:[module_num:2|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\x5D] + Select the input image:entropy_image + Name the output image:entropy_image_binary + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Select the thresholding method:Manual + Manual threshold:0.01 + Lower and upper bounds on threshold:0.000000,1.000000 + Threshold correction factor:1 + Approximate fraction of image covered by objects?:0.01 + Select the input objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:entropy_image + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):50,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Binary image + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:No + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.1 + Select binary image:entropy_image_binary + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split Fibrin and Collagen into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:Fibrin + Stain:Custom + Red absorbance:0.165976 + Green absorbance:0.958802 + Blue absorbance:0.230544 + Image name:Collagen + Stain:Custom + Red absorbance:0.724924 + Green absorbance:0.646672 + Blue absorbance:0.23728 + +MaskImage:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:Fibrin + Name the output image:Tissue_Masked_MSB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_MSB-channel + Name the primary objects to be identified:MSB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:MSB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:Fibrin + Name the output image:MSB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:MSB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:MSB_object_yellow + Select outline color:Yellow + +SaveImages:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:MSB_object_yellow + +ExportToDatabase:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/FIBRIN.area.V1.cppipe b/pipeLines/_legacy_archived/FIBRIN.area.V1.cppipe new file mode 100755 index 0000000..1a08484 --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN.area.V1.cppipe @@ -0,0 +1,331 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain "tile.tissue.bmp") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'entropy_image\'\x3A \'Y\', \'Original\'\x3A \'Y\'}, {\'entropy_image\'\x3A \'X\', \'Original\'\x3A \'X\'}, {\'entropy_image\'\x3A \'NR\', \'Original\'\x3A \'NR\'}, {\'entropy_image\'\x3A \'STAIN\', \'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain "tile.tissue.bmp") + Name to assign these images:entropy_image + Name to assign these objects:Nucleus + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ApplyThreshold:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:entropy_image + Name the output image:entropy_image_binary + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.000000,1.000000 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.01 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Tissue area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:entropy_image + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):50,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.1 + Select the measurement to threshold with:None + Select binary image:entropy_image_binary + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split Fibrin and Collagen into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:Fibrin + Stain:Custom + Red absorbance:0.165976 + Green absorbance:0.958802 + Blue absorbance:0.230544 + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Fibrin + Name the output image:Tissue_Masked_MSB + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_MSB + Name the primary objects to be identified:MSB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:MSB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Fibrin + Name the output image:MSB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:MSB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:MSB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C/Users/swvanderlaan/PLINK/analyses/expressscan/expressscan + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:MSB_object_yellow + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:27|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Database type:SQLite + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object, a single object table or a single object view?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images for which you want to save thumbnails: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Properties image group count:1 + Properties group field count:1 + Properties filter field count:0 + Workspace measurement count:1 + Experiment name:FIBRIN + Which objects should be used for locations?:None + Enter a phenotype class table name if using the classifier tool: + Export object relationships?:No + Overwrite without warning?:Data only + Access CPA images via URL?:No + Select the classification type:Object + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No + Create a CellProfiler Analyst workspace file?:No + Select the measurement display tool:ScatterPlot + Type of measurement to plot on the X-axis:Image + Enter the object name:Image + Select the X-axis measurement: + Select the X-axis index:ImageNumber + Type of measurement to plot on the Y-axis:Image + Enter the object name:Image + Select the Y-axis measurement: + Select the Y-axis index:ImageNumber diff --git a/pipeLines/_legacy_archived/FIBRIN.area.V2.cppipe b/pipeLines/_legacy_archived/FIBRIN.area.V2.cppipe new file mode 100755 index 0000000..1a08484 --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN.area.V2.cppipe @@ -0,0 +1,331 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:No filtering + Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "\x5B\\\\\\\\\\\\\\\\/\x5D\\\\\\\\.") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain "tile.tissue.bmp") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'entropy_image\'\x3A \'Y\', \'Original\'\x3A \'Y\'}, {\'entropy_image\'\x3A \'X\', \'Original\'\x3A \'X\'}, {\'entropy_image\'\x3A \'NR\', \'Original\'\x3A \'NR\'}, {\'entropy_image\'\x3A \'STAIN\', \'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain "tile.tissue.bmp") + Name to assign these images:entropy_image + Name to assign these objects:Nucleus + Select the image type:Grayscale image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ApplyThreshold:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:entropy_image + Name the output image:entropy_image_binary + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:No smoothing + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.000000,1.000000 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.01 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Tissue area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:entropy_image + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):50,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.1 + Select the measurement to threshold with:None + Select binary image:entropy_image_binary + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split Fibrin and Collagen into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:Fibrin + Stain:Custom + Red absorbance:0.165976 + Green absorbance:0.958802 + Blue absorbance:0.230544 + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Fibrin + Name the output image:Tissue_Masked_MSB + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_MSB + Name the primary objects to be identified:MSB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:MSB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Fibrin + Name the output image:MSB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:MSB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:MSB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C/Users/swvanderlaan/PLINK/analyses/expressscan/expressscan + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:MSB_object_yellow + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:27|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Database type:SQLite + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object, a single object table or a single object view?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images for which you want to save thumbnails: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Properties image group count:1 + Properties group field count:1 + Properties filter field count:0 + Workspace measurement count:1 + Experiment name:FIBRIN + Which objects should be used for locations?:None + Enter a phenotype class table name if using the classifier tool: + Export object relationships?:No + Overwrite without warning?:Data only + Access CPA images via URL?:No + Select the classification type:Object + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No + Create a CellProfiler Analyst workspace file?:No + Select the measurement display tool:ScatterPlot + Type of measurement to plot on the X-axis:Image + Enter the object name:Image + Select the X-axis measurement: + Select the X-axis index:ImageNumber + Type of measurement to plot on the Y-axis:Image + Enter the object name:Image + Select the Y-axis measurement: + Select the Y-axis index:ImageNumber diff --git a/pipeLines/_legacy_archived/FIBRIN.cp220.v1.cppipe b/pipeLines/_legacy_archived/FIBRIN.cp220.v1.cppipe new file mode 100755 index 0000000..4ea3a11 --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN.cp220.v1.cppipe @@ -0,0 +1,295 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:FIBRIN + Stain:Custom + Red absorbance:0.345301 + Green absorbance:0.854273 + Blue absorbance:0.388568 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:FIBRIN + Name the primary objects to be identified:Fibrin_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Fibrin_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:Fibrin_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:No + Text to append to the image name: + Saved file format:jpg + Output file location:Elsewhere...\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + diff --git a/pipeLines/_legacy_archived/FIBRIN_V0.cppipe b/pipeLines/_legacy_archived/FIBRIN_V0.cppipe new file mode 100644 index 0000000..98cc696 --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN_V0.cppipe @@ -0,0 +1,294 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:FIBRIN + Stain:Custom + Red absorbance:0.345301 + Green absorbance:0.854273 + Blue absorbance:0.388568 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:FIBRIN + Name the primary objects to be identified:Fibrin_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Fibrin_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:Fibrin_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Input Folder sub-folder\x7CDesktop/FIBRINTEST/cp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:No + Text to append to the image name: + Saved file format:jpg + Output file location:Elsewhere...\x7CDesktop/FIBRINTEST/cp_output + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi diff --git a/pipeLines/_legacy_archived/FIBRIN_V1.cppipe b/pipeLines/_legacy_archived/FIBRIN_V1.cppipe new file mode 100755 index 0000000..4ea3a11 --- /dev/null +++ b/pipeLines/_legacy_archived/FIBRIN_V1.cppipe @@ -0,0 +1,295 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.165 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:FIBRIN + Stain:Custom + Red absorbance:0.345301 + Green absorbance:0.854273 + Blue absorbance:0.388568 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:FIBRIN + Name the primary objects to be identified:Fibrin_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,20000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.6,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Fibrin_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:Fibrin_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:No + Text to append to the image name: + Saved file format:jpg + Output file location:Elsewhere...\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + diff --git a/pipeLines/_legacy_archived/GLYCC.area.V1.cp b/pipeLines/_legacy_archived/GLYCC.area.V1.cp new file mode 100755 index 0000000..b75c490 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCC.area.V1.cp @@ -0,0 +1,263 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/GLYCC.cp220.v1.1.cppipe b/pipeLines/_legacy_archived/GLYCC.cp220.v1.1.cppipe new file mode 100755 index 0000000..7d7d766 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCC.cp220.v1.1.cppipe @@ -0,0 +1,309 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?PAE\\\\d*?)\\\\.(?P(UMC\x7CT.*?))\\\\.(?P.*?)\\\\.(?P\\\\d*?)\\\\.X(?P\\\\d+)\\\\.Y(?P\\\\d+).*$ + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify Tissue area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB (yellow) and area (red).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/GLYCC.cp220.v1.cppipe b/pipeLines/_legacy_archived/GLYCC.cp220.v1.cppipe new file mode 100755 index 0000000..12cec82 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCC.cp220.v1.cppipe @@ -0,0 +1,309 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?PAE\\\\d*?)\\\\.(?P(UMC\x7CT.*?))\\\\.(?P.*?)\\\\.(?P\\\\d*?)\\\\.X(?P\\\\d+)\\\\.Y(?P\\\\d+).*$ + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/GLYCOPHORIN.HPC.cppipe b/pipeLines/_legacy_archived/GLYCOPHORIN.HPC.cppipe new file mode 100755 index 0000000..9424c23 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCOPHORIN.HPC.cppipe @@ -0,0 +1,347 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?PAE\\\\d*?)\\\\.(?P(UMC\x7CT.*?))\\\\.(?P.*?)\\\\.(?P\\\\d*?)\\\\.X(?P\\\\d+)\\\\.Y(?P\\\\d+).*$ + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToDatabase:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:27|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object, a single object table or a single object view?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images for which you want to save thumbnails: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Properties image group count:1 + Properties group field count:1 + Properties filter field count:0 + Workspace measurement count:1 + Experiment name:MyExpt + Which objects should be used for locations?:None + Enter a phenotype class table name if using the classifier tool: + Export object relationships?:No + Overwrite without warning?:Data only + Access CPA images via URL?:No + Select the classification type:Object + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No + Create a CellProfiler Analyst workspace file?:No + Select the measurement display tool:ScatterPlot + Type of measurement to plot on the X-axis:Image + Enter the object name:Image + Select the X-axis measurement: + Select the X-axis index:ImageNumber + Type of measurement to plot on the Y-axis:Image + Enter the object name:Image + Select the Y-axis measurement: + Select the Y-axis index:ImageNumber + diff --git a/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe b/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe new file mode 100755 index 0000000..8feb4a8 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe @@ -0,0 +1,326 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160503183100 +GitHash:ac0529e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):10,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.45,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:#F7F40A + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:True] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe_old b/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe_old new file mode 100755 index 0000000..12cec82 --- /dev/null +++ b/pipeLines/_legacy_archived/GLYCOPHORIN_SPREADSHEET.HPC.cppipe_old @@ -0,0 +1,309 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160308191757 +GitHash:b91da8e +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain ".tile.tissue.png") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?PAE\\\\d*?)\\\\.(?P(UMC\x7CT.*?))\\\\.(?P.*?)\\\\.(?P\\\\d*?)\\\\.X(?P\\\\d+)\\\\.Y(?P\\\\d+).*$ + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'---\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\'}, {\'Original\'\x3A \'X\'}, {\'Original\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:1 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'---\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +Smooth:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:16.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.05 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE_channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_Masked_DAB_channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):6,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.15,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...\x7C + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :Tissue_object\x7CNumber_Object_Number,Tissue_object\x7CLocation_Center_Y,Tissue_object\x7CLocation_Center_X,Image\x7CCount_Tissue_object,Image\x7CCount_DAB_object,Image\x7CAreaOccupied_Perimeter_Tissue_object_green,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_Tissue_object_green,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_Tissue_object_green,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CWidth_Original,Image\x7CFileName_Original,Image\x7CHeight_Original,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,DAB_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:GLYCC_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/HE.cp220.v1.cppipe b/pipeLines/_legacy_archived/HE.cp220.v1.cppipe new file mode 100755 index 0000000..245f8c7 --- /dev/null +++ b/pipeLines/_legacy_archived/HE.cp220.v1.cppipe @@ -0,0 +1,281 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:HE + Stain:Hematoxylin + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + Name the output name:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.27,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:HE_Nuclei + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:HE_Nuclei + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/_legacy_archived/HE_V0.cppipe b/pipeLines/_legacy_archived/HE_V0.cppipe new file mode 100644 index 0000000..034b095 --- /dev/null +++ b/pipeLines/_legacy_archived/HE_V0.cppipe @@ -0,0 +1,280 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:HE + Stain:Hematoxylin + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + Name the output name:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.27,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:HE_Nuclei + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:HE_Nuclei + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/HE_V1.cppipe b/pipeLines/_legacy_archived/HE_V1.cppipe new file mode 100755 index 0000000..245f8c7 --- /dev/null +++ b/pipeLines/_legacy_archived/HE_V1.cppipe @@ -0,0 +1,281 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:HE + Stain:Hematoxylin + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + Name the output name:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):8,20 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.27,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:HE_Nuclei + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:HE_Nuclei + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/_legacy_archived/SMA.V1.cp b/pipeLines/_legacy_archived/SMA.V1.cp new file mode 100755 index 0000000..ecd515d --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.V1.cp @@ -0,0 +1,329 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +UnmixColors:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Select outlines to display:HE_object_green + Select outline color:Green + +SaveImages:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToDatabase:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/SMA.V3.cppipe b/pipeLines/_legacy_archived/SMA.V3.cppipe new file mode 100755 index 0000000..480c339 --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.V3.cppipe @@ -0,0 +1,422 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Tab + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SMA_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/_legacy_archived/SMA.area.V1.cp b/pipeLines/_legacy_archived/SMA.area.V1.cp new file mode 100755 index 0000000..504de34 --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.area.V1.cp @@ -0,0 +1,255 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'11587\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' b. DAB area (yellow)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'10318\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'10465\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'10829\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'10428\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'10826\'|variable_revision_number:8|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:10|svn_version:\'10672\'|variable_revision_number:2|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + +SaveImages:[module_num:11|svn_version:\'10822\'|variable_revision_number:7|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:12|svn_version:\'10563\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + +ExportToDatabase:[module_num:13|svn_version:\'10962\'|variable_revision_number:20|show_window:False|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/SMA.area.V2.cp b/pipeLines/_legacy_archived/SMA.area.V2.cp new file mode 100755 index 0000000..54133fd --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.area.V2.cp @@ -0,0 +1,345 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' a. HE nuclei (blue)\', \' b. DAB area (yellow)\', \'3. Within DAB area (yellow) Identify\x3A\', \' a. DAB nuclei (red)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:1|show_window:True|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 + +IdentifyPrimaryObjects:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify Tissue area\'s (green)"\x5D] + Select the input image:OriginalGray_smooth + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):120,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0.05,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +UnmixColors:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D] + Stain count:2 + Color image:Original + Image name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Image name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +MaskImage:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse DAB in the previously defined Tissue\'\x5D] + Select the input image:DAB + Name the output image:Tissue_Masked_DAB-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse HE in the previously defined Tissue (green)\'\x5D] + Select the input image:Hematoxylin + Name the output image:Tissue_Masked_HE-channel + Use objects or an image as a mask?:Objects + Select object for mask:Tissue_object + Select image for mask:Tissue_object_green + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D] + Select the input image:Tissue_Masked_DAB-channel + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:8|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D] + Select the input image:Tissue_Masked_HE-channel + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.0 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:Tissue_object_green + Select outline color:Green + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Select outlines to display:nuclei_DAB_red + Select outline color:Red + +SaveImages:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:7|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.counted + Select file format to use:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Store file and path information to the saved image?:No + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure Tissue (green) and HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + +ExportToDatabase:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:20|show_window:True|notes:\x5B\'Export findings to database for further analysis\'\x5D] + Database type:MySQL / CSV + Database name:CellProfiler + Add a prefix to table names?:No + Table prefix: + SQL file prefix:SQL_ + Output file location:Default Output Folder\x7CNone + Create a CellProfiler Analyst properties file?:No + Database host:localhost + Username:cp + Password:Count! + Name the SQLite database file:DefaultDB.db + Calculate the per-image mean values of object measurements?:Yes + Calculate the per-image median values of object measurements?:Yes + Calculate the per-image standard deviation values of object measurements?:Yes + Calculate the per-well mean values of object measurements?:Yes + Calculate the per-well median values of object measurements?:Yes + Calculate the per-well standard deviation values of object measurements?:Yes + Export measurements for all objects to the database?:All + Select the objects: + Maximum # of characters in a column name:64 + Create one table per object or a single object table?:Single object table + Enter an image url prepend if you plan to access your files via http: + Write image thumbnails directly to the database?:No + Select the images you want to save thumbnails of: + Auto-scale thumbnail pixel intensities?:Yes + Select the plate type:None + Select the plate metadata:None + Select the well metadata:None + Include information for all images, using default values?:No + Hidden:1 + Hidden:1 + Hidden:0 + Select an image to include:None + Use the image name for the display?:No + Image name:Channel1 + Channel color:red + Do you want to add group fields?:No + Enter the name of the group: + Enter the per-image columns which define the group, separated by commas:ImageNumber, Image_Metadata_Plate, Image_Metadata_Well + Do you want to add filter fields?:No + Automatically create a filter for each plate?:No diff --git a/pipeLines/_legacy_archived/SMA.cp220.v3.1.cppipe b/pipeLines/_legacy_archived/SMA.cp220.v3.1.cppipe new file mode 100755 index 0000000..1d59de7 --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.cp220.v3.1.cppipe @@ -0,0 +1,477 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:18 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from \\xe2\\x80\\x9cLoad as Grayscale image\\xe2\\x80\\x9d to \\xe2\\x80\\x9cLoad as Color image\\xe2\\x80\\x9d\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find tissue using the grayscale image.\', \'White is background, non-white is foreground.\', \'Diameter is choosen after some testing.\', \'Using theshold method is based on trial and error, MoG Global does a good job of removing dark shaded area\\xe2\\x80\\x99s in contrast to Threshold.\', \'From now on, outlined foreground is called Tissue area\\xe2\\x80\\x99s (black).\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue_object + Typical diameter of objects, in pixel units (Min,Max):40,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:Tissue_object_black + Fill holes in identified objects?:Never + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Binary image + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:EntropyImage + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify Hematoxylin (nuclei) positive area\\xe2\\x80\\x99s (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Identify DAB positive area\\xe2\\x80\\x99s (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:No + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:Tissue_object_black + Select outline color:black + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\'Measure tissue (black), HE nuclei (blue) and DAB area (yellow) and nuclei (red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:5 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:Tissue_object_black + +ExportToSpreadsheet:[module_num:18|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:Yes + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SMA_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/SMA.entropy.V1.cp b/pipeLines/_legacy_archived/SMA.entropy.V1.cp new file mode 100755 index 0000000..4b46fa6 --- /dev/null +++ b/pipeLines/_legacy_archived/SMA.entropy.V1.cp @@ -0,0 +1,68 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'11587\'|variable_revision_number:11|show_window:False|notes:\x5B\'1. Find Tissue area (green)\', \'2. Within Tissue area (green) Identify\x3A\', \' b. DAB area (yellow)\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:.counted + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:Yes + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):.tile.tissue.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B^.\x5D*)\\.(?P\x5B^.\x5D*).*\\.X(?P\x5B0-9\x5D{1,4}).*\\.Y(?P\x5B0-9\x5D{1,4}) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:Original + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'10318\'|variable_revision_number:2|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +Morph:[module_num:3|svn_version:\'10300\'|variable_revision_number:2|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Scale:3 + +Smooth:[module_num:4|svn_version:\'10465\'|variable_revision_number:1|show_window:False|notes:\x5B\'Smooth away the dust using a Gaussion Filter. This is used to find Tissue (green)\'\x5D] + Select the input image:Tissue_object + Name the output image:OriginalGray_smooth + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:16.0 + Edge intensity difference:0.1 diff --git a/pipeLines/_legacy_archived/SMAPipe.v2.cppipe b/pipeLines/_legacy_archived/SMAPipe.v2.cppipe new file mode 100755 index 0000000..c8574a9 --- /dev/null +++ b/pipeLines/_legacy_archived/SMAPipe.v2.cppipe @@ -0,0 +1,416 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:17 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\\\\\.(?P\x5B^.\x5D*).*\\\\\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\\\\\.(?P\x5B^.\x5D*).*\\\\\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\', \'Please change any color images from "Load as Grayscale image" to "Load as Color image"\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:Images matching rules + Select the image type:Grayscale image + Name to assign these images:DNA + Match metadata:\x5B{\'Original\'\x3A \'Y\', u\'EntropyImage\'\x3A \'Y\'}, {\'Original\'\x3A \'X\', u\'EntropyImage\'\x3A \'X\'}, {\'Original\'\x3A \'NR\', u\'EntropyImage\'\x3A \'NR\'}, {\'Original\'\x3A \'STAIN\', u\'EntropyImage\'\x3A \'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\', \'\\xe2\\x80\\x94\', \'Settings converted from legacy pipeline.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Create gray channel, this is used to find tissue (green)\', \'Red is ussually a good channel for tissue.\', \'RGB should also be fine.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:Channels + Name the output image:OriginalGray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:No + Name the output image:OrigGreen + Convert blue to gray?:No + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1 + Image name:Channel1 + +Morph:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'Invert created gray channel\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OriginalGray + Name the output image:Tissue_object + Select the operation to perform:invert + Number of times to repeat operation:Once + Repetition number:2 + Diameter:3 + Structuring element:Disk + X offset:1 + Y offset:1 + Angle:0 + Width:3 + Height:3 + Custom:3,3,111111111 + Rescale values from 0 to 1?:Yes + +UnmixColors:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'Split HE and DAB into separate channels\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output name:DAB + Stain:DAB + Red absorbance:0.363092 + Green absorbance:0.651435 + Blue absorbance:0.666181 + Name the output name:Hematoxylin + Stain:Hematoxylin + Red absorbance:0.627888 + Green absorbance:0.685713 + Blue absorbance:0.368178 + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the primary objects to be identified:HE_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:No + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:HE_object_green + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B"Identify DAB positive area\'s (yellow)"\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the primary objects to be identified:DAB_object + Typical diameter of objects, in pixel units (Min,Max):8,5000000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Shape + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:DAB_object_yellow + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:0 + Lower and upper bounds on threshold:0.1,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MaskImage:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Only analyse Nuclei (red) in the previously defined DAB tissue (yellow)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB + Name the output image:DAB_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:DAB_object + Select image for mask:DAB + Invert the mask?:No + +MaskImage:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Hematoxylin + Name the output image:HE_object_mask + Use objects or an image as a mask?:Objects + Select object for mask:HE_object + Select image for mask:DAB + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find DAB positive nuclei (red) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:DAB_object_mask + Name the primary objects to be identified:nuclei_DAB + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_DAB_red + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +IdentifyPrimaryObjects:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:10|show_window:False|notes:\x5B\'Find HE positive nuclei (blue) within masked image (green)\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE_object_mask + Name the primary objects to be identified:nuclei_HE + Typical diameter of objects, in pixel units (Min,Max):8,40 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Shape + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:nuclei_HE_blue + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:1 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1 + Threshold correction factor:1 + Lower and upper bounds on threshold:0.3,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:From image + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +OverlayOutlines:[module_num:14|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'The next outline is ontop of the previous one.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:nuclei_HE_blue + Select outline color:Blue + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:DAB_object_yellow + Select outline color:Yellow + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:nuclei_DAB_red + Select outline color:Red + Load outlines from an image or objects?:Image + Select objects to display:None + Select outlines to display:HE_object_green + Select outline color:Green + Load outlines from an image or objects?:Image + Select objects to display:None + +SaveImages:[module_num:15|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'Save outlined images\', \'These image are usually not used for further analysis and can be stored in lossy JPEG if wanted.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder\x7CNone + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:Default + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Default Input Folder + Saved movie format:avi + +MeasureImageAreaOccupied:[module_num:16|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'Measure HE nuclei (blue) and DAB area (yellow) and red) area\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:4 + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:DAB_object_yellow + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_DAB_red + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:nuclei_HE_blue + Measure the area occupied in a binary image, or in objects?:Binary Image + Select objects to measure:None + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:HE_object_green + +ExportToSpreadsheet:[module_num:17|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder\x7C + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + :nuclei_DAB\x7CNumber_Object_Number,nuclei_DAB\x7CLocation_Center_Y,nuclei_DAB\x7CLocation_Center_X,Image\x7CCount_nuclei_DAB,Image\x7CCount_nuclei_HE,Image\x7CCount_DAB_object,Image\x7CCount_HE_object,Image\x7CAreaOccupied_Perimeter_nuclei_DAB_red,Image\x7CAreaOccupied_Perimeter_nuclei_HE_blue,Image\x7CAreaOccupied_Perimeter_DAB_object_yellow,Image\x7CAreaOccupied_Perimeter_HE_object_green,Image\x7CAreaOccupied_AreaOccupied_nuclei_DAB_red,Image\x7CAreaOccupied_AreaOccupied_nuclei_HE_blue,Image\x7CAreaOccupied_AreaOccupied_DAB_object_yellow,Image\x7CAreaOccupied_AreaOccupied_HE_object_green,Image\x7CAreaOccupied_TotalArea_nuclei_DAB_red,Image\x7CAreaOccupied_TotalArea_nuclei_HE_blue,Image\x7CAreaOccupied_TotalArea_DAB_object_yellow,Image\x7CAreaOccupied_TotalArea_HE_object_green,Image\x7CGroup_Index,Image\x7CGroup_Number,Image\x7CWidth_Original,Image\x7CWidth_EntropyImage,Image\x7CExecutionTime_10MaskImage,Image\x7CExecutionTime_13IdentifyPrimaryObjects,Image\x7CExecutionTime_01Images,Image\x7CExecutionTime_04Groups,Image\x7CExecutionTime_07UnmixColors,Image\x7CExecutionTime_15SaveImages,Image\x7CExecutionTime_02Metadata,Image\x7CExecutionTime_14OverlayOutlines,Image\x7CExecutionTime_03NamesAndTypes,Image\x7CExecutionTime_11MaskImage,Image\x7CExecutionTime_09IdentifyPrimaryObjects,Image\x7CExecutionTime_05ColorToGray,Image\x7CExecutionTime_12IdentifyPrimaryObjects,Image\x7CExecutionTime_16MeasureImageAreaOccupied,Image\x7CExecutionTime_06Morph,Image\x7CExecutionTime_08IdentifyPrimaryObjects,Image\x7CSeries_Original,Image\x7CSeries_EntropyImage,Image\x7CFrame_Original,Image\x7CFrame_EntropyImage,Image\x7CURL_EntropyImage,Image\x7CURL_Original,Image\x7CMD5Digest_Original,Image\x7CMD5Digest_EntropyImage,Image\x7CScaling_Original,Image\x7CScaling_EntropyImage,Image\x7CModuleError_10MaskImage,Image\x7CModuleError_13IdentifyPrimaryObjects,Image\x7CModuleError_06Morph,Image\x7CModuleError_07UnmixColors,Image\x7CModuleError_14OverlayOutlines,Image\x7CModuleError_08IdentifyPrimaryObjects,Image\x7CModuleError_02Metadata,Image\x7CModuleError_03NamesAndTypes,Image\x7CModuleError_11MaskImage,Image\x7CModuleError_01Images,Image\x7CModuleError_05ColorToGray,Image\x7CModuleError_12IdentifyPrimaryObjects,Image\x7CModuleError_09IdentifyPrimaryObjects,Image\x7CModuleError_04Groups,Image\x7CModuleError_15SaveImages,Image\x7CModuleError_16MeasureImageAreaOccupied,Image\x7CHeight_Original,Image\x7CHeight_EntropyImage,Image\x7CPathName_Original,Image\x7CPathName_EntropyImage,Image\x7CThreshold_OrigThreshold_nuclei_DAB,Image\x7CThreshold_OrigThreshold_nuclei_HE,Image\x7CThreshold_OrigThreshold_DAB_object,Image\x7CThreshold_OrigThreshold_HE_object,Image\x7CThreshold_SumOfEntropies_nuclei_DAB,Image\x7CThreshold_SumOfEntropies_nuclei_HE,Image\x7CThreshold_SumOfEntropies_DAB_object,Image\x7CThreshold_SumOfEntropies_HE_object,Image\x7CThreshold_WeightedVariance_nuclei_DAB,Image\x7CThreshold_WeightedVariance_nuclei_HE,Image\x7CThreshold_WeightedVariance_DAB_object,Image\x7CThreshold_WeightedVariance_HE_object,Image\x7CThreshold_FinalThreshold_nuclei_DAB,Image\x7CThreshold_FinalThreshold_nuclei_HE,Image\x7CThreshold_FinalThreshold_DAB_object,Image\x7CThreshold_FinalThreshold_HE_object,Image\x7CFileName_EntropyImage,Image\x7CFileName_Original,Image\x7CMetadata_FileLocation,Image\x7CMetadata_Series,Image\x7CMetadata_Frame,Image\x7CMetadata_Y,Image\x7CMetadata_X,Image\x7CMetadata_NR,Image\x7CMetadata_STAIN,nuclei_HE\x7CLocation_Center_Y,nuclei_HE\x7CLocation_Center_X,nuclei_HE\x7CNumber_Object_Number,Experiment\x7CModification_Timestamp,Experiment\x7CPipeline_Pipeline,Experiment\x7CRun_Timestamp,Experiment\x7CCellProfiler_Version,DAB_object\x7CNumber_Object_Number,DAB_object\x7CLocation_Center_Y,DAB_object\x7CLocation_Center_X,HE_object\x7CLocation_Center_Y,HE_object\x7CLocation_Center_X,HE_object\x7CNumber_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:SMA_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/SR.area.V1.cp b/pipeLines/_legacy_archived/SR.area.V1.cp new file mode 100755 index 0000000..0eafaad --- /dev/null +++ b/pipeLines/_legacy_archived/SR.area.V1.cp @@ -0,0 +1,234 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'# Load brightfield and polorizes SR slides\', \'Goal\x3A Measure collagen and total tissue area (SR_POLARIZED and SR)\', \'We define SR_POLARIZED as blue and SR as green\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:TIF + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:No + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):SR.scaled.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B0-9a-zA-Z\x5D*).(?P\x5B0-9a-zA-Z\x5D*).(?P.*) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:SR + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +ColorToGray:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR\', \'Create Grayscale image for finding total tissue area\'\x5D] + Select the input image:SR + Conversion method:Combine + Image type\x3A:RGB + Name the output image:SR.Gray.INV + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Red\x3A 1 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +ImageMath:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Invert grayscale image for futher analysis\'\x5D] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:SR.Gray + Image or measurement?:Image + Select the first image:SR.Gray.INV + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +ApplyThreshold:[module_num:4|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\'# SR\', \'We need to get rid of those black marker dots in the corner of the screen\', \'Therefor we want to mask these areas.\', \'A mask needs to be binary (black/white)\', "To identify the black marker dots we set a threshold to identify all black-dark area\'s"\x5D] + Select the input image:SR.Gray + Name the output image:SR.mask + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Select the thresholding method:Manual + Manual threshold:0.8 + Lower and upper bounds on threshold:0.000000,1.000000 + Threshold correction factor:1 + Approximate fraction of image covered by objects?:0.01 + Select the input objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Select the measurement to threshold with:None + +Smooth:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:1|show_window:False|notes:\x5B\'# SR\', \'We need to smooth the previous identified black marker dots a bit to fill small holes\'\x5D] + Select the input image:SR.mask + Name the output image:SR.mask + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter, in pixels:30 + Edge intensity difference:0.1 + +ApplyThreshold:[module_num:6|svn_version:\'6746\'|variable_revision_number:5|show_window:False|notes:\x5B\'# SR\', \'The smoothed mask needs to become binary again.\'\x5D] + Select the input image:SR.mask + Name the output image:SR.mask + Select the output image type:Binary (black and white) + Set pixels below or above the threshold to zero?:Below threshold + Subtract the threshold value from the remaining pixel intensities?:No + Number of pixels by which to expand the thresholding around those excluded bright pixels:0.0 + Select the thresholding method:Manual + Manual threshold:0.2 + Lower and upper bounds on threshold:0.000000,1.000000 + Threshold correction factor:1 + Approximate fraction of image covered by objects?:0.01 + Select the input objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Select the measurement to threshold with:None + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'The mask is now created, we only need to invert it (black = white and white = black)\', \'\'\x5D] + Operation:Invert + Raise the power of the result by:1 + Multiply the result by:1 + Add to result:0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:SR.mask + Image or measurement?:Image + Select the first image:SR.mask + Multiply the first image by:1 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1 + Measurement: + +MaskImage:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Here we apply the mask and use it to remove the black marker dots\'\x5D] + Select the input image:SR.Gray + Name the output image:SR.Gray.masked + Use objects or an image as a mask?:Image + Select object for mask:None + Select image for mask:SR.mask + Invert the mask?:No + +IdentifyPrimaryObjects:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'# SR\', \'Find tissue using the grayscale image.\', \'Diameter is choosen after some testing.\', \'These settings were chosen after multiple tests\'\x5D] + Select the input image:SR.Gray.masked + Name the primary objects to be identified:SR.TISSUE.green + Typical diameter of objects, in pixel units (Min,Max):10,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Otsu Global + Threshold correction factor:0.8 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.6 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:SR.TISSUE.green + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.05 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:None + +OverlayOutlines:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR\', \'Show what we defined as objects using a green outline\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:SR + Name the output image:SR.Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:SR.TISSUE.green + Select outline color:Green + +SaveImages:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR\', \'Save a copy of SR including measurements outlines in green.\'\x5D] + Select the type of image to save:Image + Select the image to save:SR.Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:SR + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.cellprofiler + Select file format to use:png + Output file location:Default Output Folder sub-folder\x7Coutline + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Store file and path information to the saved image?:Yes + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR\', \'Measure area in pixels (green and blue)\', \'Both measurements are done using the same scaled image and can there be used to calculate a ratio.\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR.TISSUE.green + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR\', "A folder \'SR\' is created in the outputfolder and contains \'Image.csv\' with your measurements", "Using \'Press to select measurements\' you can choose to export additional measurements", "Using \'Data to export\' you can choose to export addition datasets"\x5D] + Select or enter the column delimiter:Comma (",") + Prepend the output file name to the data file names?:Yes + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the columns of measurements to export?:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7CSR + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurements, using default file names?:No + Press button to select measurements to export:Image\x7CAreaOccupied_AreaOccupied_SR.TISSUE.green,Image\x7CFileName_SR,Image\x7CPathName_SR,Image\x7CMetadata_STUDYNR + Data to export:Image + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/SR.polarized.area.V1.cp b/pipeLines/_legacy_archived/SR.polarized.area.V1.cp new file mode 100755 index 0000000..89ab1be --- /dev/null +++ b/pipeLines/_legacy_archived/SR.polarized.area.V1.cp @@ -0,0 +1,164 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:1 +SVNRevision:11710 + +LoadImages:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:11|show_window:False|notes:\x5B\'# Load polarized SR slides\', \'Goal\x3A Measure polarized collagen area (SR_POLARIZED)\', \'We define SR_POLARIZED as blue\'\x5D] + File type to be loaded:individual images + File selection method:Text-Exact match + Number of images in each group?:3 + Type the text that the excluded images have in common:TIF + Analyze all subfolders within the selected folder?:All + Input image file location:Default Input Folder\x7CNone + Check image sets for missing or duplicate files?:Yes + Group images by metadata?:No + Exclude certain files?:No + Specify metadata fields to group by: + Select subfolders to analyze: + Image count:1 + Text that these images have in common (case-sensitive):SR_POLARIZED.scaled.png + Position of this image in each group:1 + Extract metadata from where?:File name + Regular expression that finds metadata in the file name:^(?P\x5B0-9a-zA-Z\x5D*).(?P\x5B0-9a-zA-Z\x5D*).(?P.*) + Type the regular expression that finds metadata in the subfolder path:.*\x5B\\\\/\x5D(?P.*)\x5B\\\\/\x5D(?P.*)$ + Channel count:1 + Group the movie frames?:No + Grouping method:Interleaved + Number of channels per group:3 + Load the input as images or objects?:Images + Name this loaded image:SR_POLARIZED + Name this loaded object:Nuclei + Retain outlines of loaded objects?:No + Name the outline image:NucleiOutlines + Channel number:1 + Rescale intensities?:Yes + +RescaleIntensity:[module_num:2|svn_version:\'6746\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Rescale intensity to re-level the brightness of each image and make further analysis more uniform.\', \'\'\x5D] + Select the input image:SR_POLARIZED + Name the output image:Original + Select rescaling method:Divide by the image\'s maximum + How do you want to calculate the minimum intensity?:Custom + How do you want to calculate the maximum intensity?:Custom + Enter the lower limit for the intensity range for the input image:0 + Enter the upper limit for the intensity range for the input image:1 + Enter the intensity range for the input image:0.000000,1.000000 + Enter the desired intensity range for the final, rescaled image:0.000000,1.000000 + Select method for rescaling pixels below the lower limit:Mask pixels + Enter custom value for pixels below lower limit:0 + Select method for rescaling pixels above the upper limit:Mask pixels + Enter custom value for pixels below upper limit:0 + Select image to match in maximum intensity:None + Enter the divisor:1 + Select the measurement to use as a divisor:None + +ColorToGray:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Create Grayscale image for finding total tissue area\', \'\'\x5D] + Select the input image:Original + Conversion method:Combine + Image type\x3A:Channels + Name the output image:SR_POLARIZED.Gray + Relative weight of the red channel:1 + Relative weight of the green channel:1 + Relative weight of the blue channel:1 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Channel count:1 + Channel number\x3A:Green\x3A 2 + Relative weight of the channel:1 + Image name\x3A:Channel1 + +IdentifyPrimaryObjects:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:8|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Find tissue using the grayscale image.\', \'Diameter is choosen after some testing.\', \'These settings are choosen after multiple tests\'\x5D] + Select the input image:SR_POLARIZED.Gray + Name the primary objects to be identified:SR_POLARIZED.TISSUE.blue + Typical diameter of objects, in pixel units (Min,Max):3,99999999 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:Yes + Discard objects touching the border of the image?:No + Select the thresholding method:Manual + Threshold correction factor:1 + Lower and upper bounds on threshold:0,1.0 + Approximate fraction of image covered by objects?:0.6 + Method to distinguish clumped objects:None + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:SR_POLARIZED.TISSUE.blue + Fill holes in identified objects?:Yes + Automatically calculate size of smoothing filter?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Manual threshold:0.1 + Select binary image:None + Retain outlines of the identified objects?:Yes + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Select the measurement to threshold with:Scaling_Original + +OverlayOutlines:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Show what we defined as objects using a blue outline\'\x5D] + Display outlines on a blank image?:No + Select image on which to display outlines:SR_POLARIZED + Name the output image:SR_POLARIZED.Overlay + Select outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:1 + Select outlines to display:SR_POLARIZED.TISSUE.blue + Select outline color:Blue + +SaveImages:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR_POLARIZED\', \'Save a copy of SR_POLARIZED including measurements outlines in blue.\'\x5D] + Select the type of image to save:Image + Select the image to save:SR_POLARIZED.Overlay + Select the objects to save:None + Select the module display window to save:None + Select method for constructing file names:From image filename + Select image name for file prefix:SR_POLARIZED + Enter single file name:OrigBlue + Do you want to add a suffix to the image file name?:Yes + Text to append to the image name:.cellprofiler + Select file format to use:png + Output file location:Default Output Folder sub-folder\x7Coutline + Image bit depth:8 + Overwrite existing files without warning?:Yes + Select how often to save:Every cycle + Rescale the images? :No + Save as grayscale or color image?:Grayscale + Select colormap:gray + Store file and path information to the saved image?:Yes + Create subfolders in the output folder?:No + +MeasureImageAreaOccupied:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:3|show_window:False|notes:\x5B\'# SR_POLARIZED & SR\', \'Measure area in pixels (green and blue)\', \'Both measurements are done using the same scaled image and can there be used to calculate a ratio.\'\x5D] + Hidden:1 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR_POLARIZED.TISSUE.blue + Retain a binary image of the object regions?:No + Name the output binary image:Stain + Select a binary image to measure:None + +ExportToSpreadsheet:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:7|show_window:False|notes:\x5B\'# SR_POLARIZED\', "A folder \'SR_Polarized\' is created in the outputfolder and contains \'Image.csv\' with your measurements", "Using \'Press to select measurements\' you can choose to export additional measurements", "Using \'Data to export\' you can choose to export addition datasets"\x5D] + Select or enter the column delimiter:Comma (",") + Prepend the output file name to the data file names?:Yes + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the columns of measurements to export?:Yes + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7CSR_POLARIZED + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurements, using default file names?:No + Press button to select measurements to export:Image\x7CAreaOccupied_AreaOccupied_SR_POLARIZED.TISSUE.blue,Image\x7CFileName_SR_POLARIZED,Image\x7CPathName_SR_POLARIZED,Image\x7CMetadata_STUDYNR + Data to export:Image + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes diff --git a/pipeLines/_legacy_archived/SR_V1.cppipe b/pipeLines/_legacy_archived/SR_V1.cppipe new file mode 100755 index 0000000..c21ff45 --- /dev/null +++ b/pipeLines/_legacy_archived/SR_V1.cppipe @@ -0,0 +1,276 @@ +CellProfiler Pipeline: http://www.cellprofiler.org +Version:3 +DateRevision:20160418141927 +GitHash:9969f42 +ModuleCount:13 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:or (file does contain ".tile.tissue.png") (file does contain "ENTROPY_") + +Metadata:[module_num:2|svn_version:\'Unknown\'|variable_revision_number:4|show_window:False|notes:\x5B\'The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:2 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:^(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:or (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression:ENTROPY_(?P\x5B^.\x5D*)\\\\.(?P\x5B^.\x5D*).*\\\\.X(?P\x5B0-9\x5D{1,4}).*\\\\.Y(?P\x5B0-9\x5D{1,4}) + Regular expression:(?P\x5B0-9\x5D{4}_\x5B0-9\x5D{2}_\x5B0-9\x5D{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file does contain "ENTROPY_") (file does contain ".bmp") (file doesnot contain ".counted") + Metadata file location: + Match file and image metadata:\x5B\x5D + Use case insensitive matching?:No + +NamesAndTypes:[module_num:3|svn_version:\'Unknown\'|variable_revision_number:6|show_window:False|notes:\x5B\'The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:\x5B{u\'Original\'\x3A u\'Y\', u\'EntropyImage\'\x3A u\'Y\'}, {u\'Original\'\x3A u\'X\', u\'EntropyImage\'\x3A u\'X\'}, {u\'Original\'\x3A u\'NR\', u\'EntropyImage\'\x3A u\'NR\'}, {u\'Original\'\x3A u\'STAIN\', u\'EntropyImage\'\x3A u\'STAIN\'}\x5D + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Select the rule criteria:and (file doesnot contain "ENTROPY_") (file doesnot contain ".counted") (file does contain ".tile.tissue.png") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + Select the rule criteria:and (file does contain "ENTROPY_") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Retain outlines of loaded objects?:No + Name the outline image:LoadedOutlines + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:\'Unknown\'|variable_revision_number:2|show_window:False|notes:\x5B\'The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.\'\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:Red\x3A 1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:FilteredImage + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:\'Unknown\'|variable_revision_number:4|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Ignore the image masks?:No + Name the output image:ImageAfterMath + Image or measurement?:Image + Select the first image:OrigGray + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:ImageAfterMath + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Manual + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.065 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Two classes + Minimize the weighted variance or the entropy?:Weighted variance + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +UnmixColors:[module_num:9|svn_version:\'Unknown\'|variable_revision_number:2|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Stain count:1 + Select the input color image:Original + Name the output name:SR + Stain:Custom + Red absorbance:0.370654 + Green absorbance:0.754688 + Blue absorbance:0.541352 + +IdentifyPrimaryObjects:[module_num:10|svn_version:\'Unknown\'|variable_revision_number:10|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:SR + Name the primary objects to be identified:SR_Tissue + Typical diameter of objects, in pixel units (Min,Max):1,100 + Discard objects outside the diameter range?:Yes + Try to merge too small objects with nearby larger objects?:No + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Name the outline image:PrimaryOutlines + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Retain outlines of the identified objects?:No + Automatically calculate the threshold using the Otsu method?:Yes + Enter Laplacian of Gaussian threshold:0.5 + Automatically calculate the size of objects for the Laplacian of Gaussian filter?:Yes + Enter LoG filter diameter:5.0 + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Threshold setting version:2 + Threshold strategy:Global + Thresholding method:Otsu + Select the smoothing method for thresholding:Automatic + Threshold smoothing scale:1.0 + Threshold correction factor:1.3 + Lower and upper bounds on threshold:0.25,1.0 + Approximate fraction of image covered by objects?:0.01 + Manual threshold:0.0 + Select the measurement to threshold with:None + Select binary image:None + Masking objects:None + Two-class or three-class thresholding?:Three classes + Minimize the weighted variance or the entropy?:Entropy + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Method to calculate adaptive window size:Image size + Size of adaptive window:10 + Use default parameters?:Default + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2.0 + +MeasureImageAreaOccupied:[module_num:11|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Hidden:2 + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:SR_Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Nuclei + Select a binary image to measure:None + Measure the area occupied in a binary image, or in objects?:Objects + Select objects to measure:Tissue + Retain a binary image of the object regions?:Yes + Name the output binary image:Tissue + Select a binary image to measure:None + +OverlayOutlines:[module_num:12|svn_version:\'Unknown\'|variable_revision_number:3|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:OrigGray + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + Width of outlines:3 + Select outlines to display:None + Select outline color:#FB1E07 + Load outlines from an image or objects?:Objects + Select objects to display:Tissue + Select outlines to display:None + Select outline color:#118402 + Load outlines from an image or objects?:Objects + Select objects to display:SR_Tissue + +ExportToSpreadsheet:[module_num:13|svn_version:\'Unknown\'|variable_revision_number:11|show_window:True|notes:\x5B\x5D|batch_state:array(\x5B\x5D, dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma (",") + Add image metadata columns to your object data file?:No + Limit output to a size that is allowed in Excel?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:No + Calculate the per-image median values for object measurements?:No + Calculate the per-image standard deviation values for object measurements?:No + Output file location:Default Output Folder sub-folder\x7Ccp_output + Create a GenePattern GCT file?:No + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:None + Export all measurement types?:Yes + : + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:Output_ + Overwrite existing files without warning?:No + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:DATA.csv + Use the object name for the file name?:Yes + diff --git a/pipeLines/readme.txt b/pipeLines/readme.txt new file mode 100644 index 0000000..e585ed6 --- /dev/null +++ b/pipeLines/readme.txt @@ -0,0 +1,29 @@ +# README + +# Version name changes + + +Date: 2021-11-21 +Author: Sander W. van der Laan + +Action: Changed version names + +### GLYCC: Changed version name for the right GLYCC pipeline. + +GLYCC.cp413.imageasmask.v1.0.cppipe > GLYCC.cp413.v1.0.cppipe + exports png, export all data, + analyses only normalized tiles +GLYCC.cp413.imageasmask.v1.0.cpproj > GLYCC.cp413.v1.0.cpproj +GLYCC.cp413.imageasmask.v1.0.imageset_tested.csv > GLYCC.cp413.v1.0.imageset_tested.csv +GLYCC.cp413.imageasmask.v1.0.txt > GLYCC.cp413.v1.0.txt + +Note that the analyses in Mekke JM et al medRxiv 2021 were done using the above pipeline, +under the 'GLYCC.cp413.imageasmask.v1.0.cppipe' filename. + + +### HE: Changed version name for the right HE pipeline. + +HE.cp413.v1.0.cppipe > file name to png, exports all data, only uses normalized + tiles +HE.cp413.v0.9.cppipe > file name to png, exports all data, + counts entropy filtered data, and normal tiles \ No newline at end of file diff --git a/python_scribbles/README.md b/python_scribbles/README.md new file mode 100755 index 0000000..7177407 --- /dev/null +++ b/python_scribbles/README.md @@ -0,0 +1,6 @@ +Python Scribbles +============ + +Some python scripts for some simple tasks. These are not needed for the slideToolKit workflow +by may come in handy at other times when handling whole-slide images. + diff --git a/python_scribbles/slideNDPI2Thumb.py b/python_scribbles/slideNDPI2Thumb.py new file mode 100755 index 0000000..266ea27 --- /dev/null +++ b/python_scribbles/slideNDPI2Thumb.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python +# script to extract a macro-file from NDPI file + +# Also check: https://www.pyimagesearch.com/2019/01/30/macos-mojave-install-tensorflow-and-keras-for-deep-learning/ +# pip install importlib-metadata +# pip install scipy pillow imutils h5py requests progressbar2 scikit-learn scikit-image numpy pandas +# conda install -c anaconda sqlite ncurses readline +# conda install -c bioconda openslide +# conda install -c anaconda scikit-image + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideNDPI2Thumb: NDPI to Thumbnail ") +print("") +print("* Version : v1.0.0") +print("") +print("* Last update : 2020-12-28") +print("* Written by : Nikolas Stathonikos | nstatho2@umcutrecht.nl") +print("* Edited by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("") +print("* Description : This script will get Thumbnails from NDPI images for quick inspection. ") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +### ADD-IN: +### - requirement check +### - if not present install +### - report that the requirements are met +### - make argument parser +### - put in copyright statement + +# import required packages +import sys +import glob +import os +import numpy as np + +# for argument parser +import argparse +import textwrap + +# to process NDPI images +import openslide +from PIL import Image +import skimage.io +from skimage import img_as_uint + +# evaluate arguments +parser = argparse.ArgumentParser( + prog='slideNDPI2Thumb', + description='This script will get Thumbnails from NDPI images in a given folderfor quick inspection.', + usage='slideNDPI2Thumb [-h/--help] -p/--path PATH', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl; Nikolas Stathonikos | nstatho2@umcutrecht.nl")) +parser.add_argument('-p','--path', help='Give the full path to where the stain directory resides, e.g. /data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/', required=True) + +try: + args = parser.parse_args() + print("We are extracting Thumbnails from images in [" + args.path + "].") + +except SystemExit: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideNDPI2Thumb ***!\n") + parser.print_help() + exit() + +# globals +PATHS = args.path + +def extractMacro(slide): + img = openslide.OpenSlide(slide) + im = img.associated_images['macro'] +# tim = img.get_thumbnail(800,600) + + # gray scale macro + # print("* DEBUG: Get gray scaled images.") + macro_image = np.asarray(im.convert('L')) + + # RGB macro + # print("* DEBUG: Get RGB scaled images.") + macro_save = np.asarray(im.convert('RGB')) + + # RGB macro + # print("* DEBUG: Get RGB scaled images.") +# thumb_save = np.asarray(im.convert()) + + skimage.io.imsave("{0}.macro.png".format(img._filename), macro_save) +# skimage.io.imsave("{0}.thumbnail.png".format(img._filename), thumb_save) + print("* Exported {0}".format( img._filename + '.[thumbnail/macro].png')) + return macro_image + +print("Starting extractions.") +if __name__ == "__main__": + + directory = PATHS + for slide in glob.glob(os.path.join(directory, '*.ndpi')): + macro_image = extractMacro(slide) + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2020 Nikolas Stathonikos, Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/python_scribbles/slideNDPytools.py b/python_scribbles/slideNDPytools.py new file mode 100755 index 0000000..7a5dafb --- /dev/null +++ b/python_scribbles/slideNDPytools.py @@ -0,0 +1,309 @@ +#!/usr/bin/env python +# +# Ref: https://github.com/fepegar/ndPytools/blob/master/ndpitools.py +# + +import os +import re +import shutil +import subprocess +from subprocess import Popen, PIPE, STDOUT + +import numpy as np + +NUMBER_OF_MAGNIFICATIONS = 5 +MAX_TILE_SIZE = 20000 # 20000*20000*3 = 1.2 GB RAM +COMPRESSION_NONE = 'n' +COMPRESSION_JPEG = 'j' + +class NDPI: + + def __init__(self, filepath, debug=False): + if not filepath.endswith('.ndpi'): + raise IOError(os.path.basname(filepath) + ' is not an NDPI file.') + elif not os.path.exists(filepath): + raise IOError(filepath + ' does not exist.') + else: + self.filepath = filepath + self.readInfoFromHeader() + self.commandLine = [] + self.debug = debug + + + def __repr__(self): + s = ['Nanozoomer Digital Pathology Image'] + s.append(' Source lens: x%d' % self.sourceLens) + s.append(' Size: %d x %d pixels' % (self.size[0], self.size[1])) + s.append(' Resolution: %d x %d pixels/cm2' % (self.resolution[0], self.resolution[1])) + s.append(' Pixel spacing: %.3f x %.3f nm' % (self.spacing[0]*1e6, self.spacing[1]*1e6)) + return '\n'.join(s) + + + def readInfoFromHeader(self): + cmd = ['tifftopnm', '-headerdump', self.filepath] + process = Popen(cmd, stdout=PIPE, stderr=STDOUT) + text = process.communicate()[0] + + pattern = r'Image Width: (\d+) Image Length: (\d+)' + numbersStrings = re.findall(pattern, text) + self.size = np.array([int(n) for tup in numbersStrings for n in tup]) + + pattern = r'Tag 65421: (\d+)' + numbersStrings = re.findall(pattern, text) + self.sourceLens = int(numbersStrings[0]) + self.magnifications = [self.sourceLens/4.**i for i in range(NUMBER_OF_MAGNIFICATIONS)] + self.magnifications = [int(x) if x%1 == 0 else x for x in self.magnifications] # E. g. 20.0 -> 20 + + pattern = r'Resolution: (\d+), (\d+) pixels/cm' + numbersStrings = re.findall(pattern, text) + resolution = np.array([int(n) for tup in numbersStrings for n in tup]) + self.resolution = resolution + self.spacing = 10./resolution # 10 mm + + + def getMagnificationsString(self): + s = ['x%s'%n for n in self.magnifications] + return ', '.join(s) + + + def split(self, magnifications=[], outputPath=None, compression=COMPRESSION_JPEG, run=True): + commandLine = ['ndpisplit'] + if magnifications: + if not isinstance(magnifications, list): + magnificationsString = '-x' + str(magnifications) + else: + magnificationsString = '-x' + for magnification in magnifications: + magnificationsString += str(magnification) + ',' + + commandLine.append(magnificationsString.strip(',')) + commandLine.append('-c' + compression) + commandLine.append(self.filepath) + self.commandLine = commandLine + if run: + self.run() + tiffPath = self.getMagnificationFilepath(magnifications) + if outputPath is None: # we suppose there is only one magnification + outputPath = tiffPath + else: + src, dst = tiffPath, outputPath + if os.path.exists(src): + shutil.move(src, dst) + return outputPath + + + def extractROI(self, magnification, topLeftX, topLeftY, width, height, outputPath=None, compression=COMPRESSION_JPEG, run=True): + if magnification not in self.magnifications: + raise ValueError('Magnification x%s is not available. Choose between the following: %s' % (magnification, self.getMagnificationsString())) + magnificationSize = self.getSize(magnification) + inX = topLeftX < magnificationSize[0] and width <= magnificationSize[0] and (topLeftX + width) <= magnificationSize[0] + 1 + inY = topLeftY < magnificationSize[1] and height <= magnificationSize[1] and (topLeftY + height) <= magnificationSize[1] + 1 + + commandLine = ['ndpisplit'] + options = '-Ex%s,%d,%d,%d,%d' % (str(magnification), topLeftX, topLeftY, width, height) + commandLine.append(options) + commandLine.append('-c' + compression) + commandLine.append(self.filepath) + # print magnificationSize + # print topLeftX, topLeftY, width, height + # print commandLine + # print inX + # print inY + if not (inX and inY): + raise ValueError('Wrong ROI size. The image size for this magnification is %s.' % self.getSizeString(magnificationSize)) + self.commandLine = commandLine + if run: + self.run() + roiPath = self.getROIFilepath(magnification) + if outputPath is None: + return roiPath + else: + src, dst = roiPath, outputPath + if os.path.exists(src): + shutil.move(src, dst) + return dst + + + def getSizeString(self, size): + return '%d x %d' % (size[0], size[1]) + + + def getAffine(self, magnification=None, sz=0.05, oz=0): + if magnification is None: + magnification = self.sourceLens + sx, sy = self.getSpacing(magnification) + affine = np.diag([sx, sy, sz, 1]) + affine[2, 3] = oz + sizeX, sizeY = self.getSize(magnification) + affine[0, 3] = sizeX * sx + affine[0, 0] *= -1 + return affine + + + def extractROIFromFile(self, acsvPath, magnification=None, outputDir=None, prefix=None, oz=0, flipX=False, flipY=False, removeTIFF=True, makeNifti=True): + roiCenterWorld, roiSizeWorld = acsv.ROI(acsvPath).getCenterAndSize() + self.extractROIFromCenterAndSize(roiCenterWorld, roiSizeWorld, magnification=magnification, outputDir=outputDir, prefix=prefix, oz=oz, flipX=flipX, flipY=flipY, removeTIFF=removeTIFF, makeNifti=makeNifti) + + + def extractROIFromCenterAndSize(self, roiCenterWorld, roiSizeWorld, magnification=None, outputDir=None, prefix=None, oz=0, flipX=False, flipY=False, removeTIFF=True, makeNifti=True): + import nibabel as nib + import acsv + import utils + + if magnification is None: + magnification = self.sourceLens + ijk2ras = self.getAffine(magnification) + + ras2ijk = np.linalg.inv(ijk2ras) + roiCenterPixel = nib.affines.apply_affine(ras2ijk, roiCenterWorld) + sx, sy, sz, _ = np.diag(ijk2ras) + roiSizePixel = np.abs(roiSizeWorld / np.array((sx, sy, sz))) + + topLeft = np.round(roiCenterPixel - roiSizePixel/2).astype(int) + topRight = np.round(roiCenterPixel + roiSizePixel/2).astype(int) + topRightX = topRight[0] + topLeftX, topLeftY, _ = topLeft + width, height, _ = np.round(roiSizePixel).astype(int) + + if flipX: + topLeftX = self.flip(topRightX, 0, magnification) + topRightX = self.flip(topLeftX, 0, magnification) + if flipY: + topLeftY = self.flip(topLeftY, 1, magnification) - height + + + numTilesX = width / MAX_TILE_SIZE + 1 + numTilesY = height / MAX_TILE_SIZE + 1 + numTiles = numTilesX * numTilesY + + tileWidth = width / numTilesX + tileHeight = height / numTilesY + + if outputDir is None: + outputDir = os.path.dirname(self.filepath) + if prefix is None: + prefix = os.path.splitext(os.path.basename(self.filepath))[0] + + for tileY in range(numTilesY): + for tileX in range(numTilesX): + tileTopLeftX = topLeftX + tileX*tileWidth + tileTopLeftY = topLeftY + tileY*tileHeight + tileTopRightX = tileTopLeftX + tileWidth - 1 + + if flipX: + tileColumn = numTilesX - tileX - 1 + else: + tileColumn = tileX + + if flipY: + tileRow = numTilesY - tileY - 1 + else: + tileRow = tileY + + if numTiles == 1: + outputPath = os.path.join(outputDir, prefix + '.tif') + else: + outputPath = os.path.join(outputDir, prefix + '_tile_%d_%d.tif' % (tileRow, tileColumn)) + + utils.ensureDir(outputPath) + roiPath = self.extractROI(magnification, tileTopLeftX, tileTopLeftY, tileWidth, tileHeight, outputPath=outputPath) + + if makeNifti: + import ImageUtils as iu + roiAffine = ijk2ras[:] + roiAffine[:3, :3] = np.abs(roiAffine[:3, :3]) + roiAffine[0, 3] = self.flip(tileTopRightX, 0, magnification) * roiAffine[0, 0] + roiAffine[1, 3] = tileTopLeftY * roiAffine[1, 1] + + if flipX: + roiAffine[0, 0] *= -1 + roiAffine[0, 3] = abs(roiAffine[0, 0]) * (tileTopLeftX + tileWidth) + if flipY: + roiAffine[1, 1] *= -1 + roiAffine[1, 3] = abs(roiAffine[1, 1]) * self.flip(tileTopLeftY, 1, magnification) + + roiAffine[2, 3] = oz + iu.histologyImageToNiftiRGB(roiPath, affine=roiAffine) + + if removeTIFF: + os.remove(roiPath) + + + def flip(self, n, dim, magnification=None): + if magnification is None: + magnification = self.getSourceLens() + sizeDim = self.getSize(magnification)[dim] + return sizeDim - n - 1 + + + def getMagnificationFilepath(self, magnification): + base = os.path.splitext(self.filepath)[0] + magPath = '{}_x{}_z0.tif'.format(base, magnification) + return magPath + + + def getROIFilepath(self, magnification): + base = os.path.splitext(self.filepath)[0] + magPath = '{}_x{}_z0_1.tif'.format(base, magnification) + return magPath + + + def getSize(self, magnification=None): + if magnification is None: + magnification = self.sourceLens + return map(int, self.size / (self.sourceLens / magnification)) + + + def getSpacing(self, magnification=None): + if magnification is None: + magnification = self.sourceLens + return self.spacing * (self.sourceLens / magnification) + + + def getSourceLens(self): + return self.sourceLens + + + def getROISize(self, acsvPath): + import acsv + _, _, width, height = acsv.ROI(acsvPath).getCropValuesWorld() + spacingX, spacingY = self.getSpacing() + widthPixels = np.round(width / spacingX).astype(int) + heightPixels = np.round(height / spacingY).astype(int) + return widthPixels, heightPixels + + + def getROIMemory(self, acsvPath): + widthPixels, heightPixels = self.getROISize(acsvPath) + numPixels = widthPixels * heightPixels + numBytes = numPixels * 3 + if numBytes > 1e9: + print('%.3f' % (numBytes/1e9), 'GB') + else: + print('%d' % (numBytes/1e6), 'MB') + + + def getSplitRatio(self, remove=True): + import ImageUtils as iu + lowestMagnification = self.magnifications[-2] # Sometimes the lowest magnification image is truncated + imgPath = self.split(lowestMagnification, compression=COMPRESSION_NONE) + splitRatio = iu.getSplitLeftRightColumnRatio(imgPath) + if remove: + os.remove(imgPath) + return splitRatio + + + def printCommandLine(self): + print(' '.join(self.commandLine)) + + + def run(self): + if self.debug: + print('Running:') + self.printCommandLine() + subprocess.call(self.commandLine) + + +def getSplitRatio(filepath, remove=True): + ndpiFile = NDPI(filepath) + return ndpiFile.getSplitRatio(remove=remove) diff --git a/python_scribbles/slideRename.bash2py.py b/python_scribbles/slideRename.bash2py.py new file mode 100755 index 0000000..8386b0a --- /dev/null +++ b/python_scribbles/slideRename.bash2py.py @@ -0,0 +1,574 @@ +#!/usr/bin/env python +# script to display a whole-slide image file and manually rename the file (*.TIF, *.NDPI, etc.) +# +# Ref: https://github.com/syuanca/bash2python +# + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideRename: display and manually rename images ") +print("") +print("* Version : v1.0.0") +print("") +print("* Last update : 2022-06-13") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("") +print("* Description : This script will get thumbnails from (a list of given) images for quick inspection.") +print(" Converted using https://github.com/syuanca/bash2python") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + + +import os +import sys +import subprocess +import string +import random + +bashfile=''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10)) +bashfile='/tmp/'+bashfile+'.sh' + +f = open(bashfile, 'w') +s = """#!/bin/bash +# +# Description: Batch rename slides +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +# Variables +SCRIPTNAME=$(basename $0) +DESCRIPTIONSHORT="Batch rename slides" +DEPENDENCIES=("awk" "convert" "dmtxread" "grep" "identify" "display" "perl" "tiffinfo" "wmctrl" "zbarimg") +SUPPORTED_FORMATS=("Generic-TIFF" "Aperio" "Hamamatsu" "iScan" "iScanHT" "Leica") +DEFAULT_RESIZE="512x1024" +DEFAULT_OUTPUT_PREFIX="" # empty by default +DEFAULT_OUTPUT_SUFFIX="" # empty by default +DEFAULT_IM_ARGS="" # imagemagick arguments # +dither -colors 2 -colorspace gray +DEFAULT_WMCTRL_TIMEOUT="0.1" # timeout for window switching when using im-display +DEFAULT_DMTXREAD_ARGS="--stop-after 1 --milliseconds 1000 --shrink 2 --square-deviation 10" # options for dmtxread. See man dmtxread for details +DEFAULT_ZBARIMG_ARGS="--quiet " # options for zbarimg. --quiet for barcode only. See man dmtxread for details + + +# sort arrays +DEPENDENCIES=( $( for el in "${DEPENDENCIES[@]}"; do echo "$el"; done | sort) ) +SUPPORTED_FORMATS=( $( for el in "${SUPPORTED_FORMATS[@]}"; do echo "$el"; done | sort) ) + +# Cleanup stage +set -e +function cleanup { + # kill display like a real man if alive + if [[ $IM_DISPLAY_PID > 0 ]]; then + kill $IM_DISPLAY_PID + fi + # quit Preview like a polite man if it is still alive. + if [[ "$PREVIEW" == "osx" ]];then # did we use preview at all? + if [[ $(ps ux | grep -i [P]review | wc -l) != 0 ]]; then + osascript -e "ignoring application responses" -e "tell application \\"Preview\\"" -e "if it is running then" -e "quit with saving" -e "end if" -e "end tell" -e "end ignoring" + fi + fi + + # remove temp files + rm -rf "$PREVIEW_TMPFILE" +} +trap cleanup EXIT + +# Errors go to stderr +err() { + echo "${SCRIPTNAME} ERROR: $@" >&2 +} + +# usage message +usage() { +cat <<- EOF +$SCRIPTNAME --help for more information. +EOF +} + +# version info +version() { +cat << EOF +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) + +The MIT License (MIT) +Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be included +in all copies or substantial portions of the Software. + +There is NO WARRANTY, to the extent permitted by law. + +Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + +EOF +} + + +# help message +helpMessage() { +cat <<- EOF +${SCRIPTNAME}: ${DESCRIPTIONSHORT} + +usage: + $SCRIPTNAME [options] -f + +options: + -f, --file virtual slide to rename + + [-l, --layer] force specific layer by layer id + [--prefix] filename prefix + [--suffix] filename suffix + [--to-lower] filename output is transformed to lower cases + [--to-upper] filename output is transformed to upper cases + [--barcode dmtx|zbar] find barcode (by library) and rename accordingly. + [--resize] preview dimentions ["$DEFAULT_RESIZE"] + [--convert-args]='' convert arguments ('=' sign and 'quotes' required) + [--preview im|osx|none] display preview miniature (imageMagick, OS X Preview.app, none) + [--wmctrl-timeout] window switching timeout ["$DEFAULT_WMCTRL_TIMEOUT"](see description) + [--ignore-dependencies] ignores all dependencies, but gives warning + + --help display this help + --version display version and license information + + +examples: + $SCRIPTNAME "file.tif" + $SCRIPTNAME --prefix="AE" --suffix=".CD68.\\${RANDOM}" --file="file.tif" + $SCRIPTNAME --layer=5 --convert-args="-rotate 90" -f "file.tif" + +print a job list -- for automatic renaming: + find "\\$(pwd)" -iname "*.tif" \\\\ + -exec "$(command -v "${SCRIPTNAME}")" \\\\ + --barcode="dmtx" --to-upper --preview="none" -f="{}" \\; + +print a job list -- for manual renaming: + find "\\$(pwd)" -iname "*.tif" \\\\ + -exec "$(command -v "${SCRIPTNAME}")" \\\\ + --to-upper --preview="osx" -f="{}" \\; + + +dependencies: ${DEPENDENCIES[@]} +supported formats: ${SUPPORTED_FORMATS[@]} + +This script displays a miniture of the whole slide and asks for a new +name. The --convert-args overrides all other convert arguments that are +already set (like --resize). For some slides, the virtual slide format +is not known. For these instances you need to run slideInfo to find the +correct layer manually. When the layer containing the label is +identified, use the --layer flag. Using a slow system can cause the +focus switching to fail, if so, set the '--wmctrl-timeout' to a higher +number (e.g. 1.0). On OS X, by default, Preview.app is used for thumb +previews. The '--preview' option only works properly from an X11 +terminal (on a Mac use '/Applications/Utilities/XQuartz.app'). Using the +--barcode flag, different libraries can be used to rename slides using +barcodes, dmtx (for Data Matrix 2D barcodes), and zlib (for +EAN-13/UPC-A, UPC-E, EAN-8, Code 128, Code 39, Interleaved 2 of 5 and QR +Code). Image manipulation can be done befor previewing the slide using +the --convert-args flag, the equal sign ('=') and surrounding quotes "'" +are required. + +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. +EOF +} + +# Menu +# Empty variables +FILE="" +LAYER="" +OUTPUT_PREFIX="" +OUTPUT_SUFFIX="" +RESIZE="" +IM_DISPLAY="" +WMCTRL_TIMEOUT="" +BARCODE="" +PREVIEW="" +# illegal option +illegalOption() { +cat <<- EOF +$SCRIPTNAME: illegal option $1 +$(usage) +EOF +exit 1 +} +# loop through options +while : +do + case $1 in + -h) + usage + exit 0 ;; + --help | -\\?) + helpMessage + exit 0 ;; + --version ) + version + exit 0 ;; + -f | --file) + FILE=$2 + shift 2 ;; + -f=* | --file=*) + FILE=${1#*=} + shift ;; + -l | --layer) + LAYER=$2 + shift 2 ;; + --layer=*) + LAYER=${1#*=} + shift ;; + --file=*) + FILE=${1#*=} + shift ;; + --prefix=*) + OUTPUT_PREFIX=${1#*=} + shift ;; + --prefix) + OUTPUT_PREFIX=$2 + shift 2 ;; + --suffix=*) + OUTPUT_SUFFIX=${1#*=} + shift ;; + --suffix) + OUTPUT_SUFFIX=$2 + shift 2 ;; + --resize=*) + RESIZE=${1#*=} + shift ;; + --resize) + RESIZE=$2 + shift 2 ;; + --to-upper) + CASE="CASE_UPPER" + shift ;; + --to-lower) + CASE="CASE_LOWER" + shift ;; + --preview=*) + PREVIEW=${1#*=} + shift ;; + --preview) + PREVIEW=$2 + shift 2 ;; + --convert-args=*) # Equal sign and quotes are required for this option. If not, the convert arguments are seen as slideRename options. + IM_ARGS=${1#*=} + shift ;; + --wmctrl-timeout) + WMCTRL_TIMEOUT=$2 + shift 2 ;; + --wmctrl-timeout=*) + WMCTRL_TIMEOUT=${1#*=} + shift ;; + --extension=*) + OUTPUT_EXTENSION=${1#*=} + shift ;; + --barcode=*) + BARCODE=${1#*=} + shift ;; + --barcode) + BARCODE=$2 + shift 2 ;; + --ignore-dependencies) + IGNOREDEPENDENCIES=TRUE + shift ;; + --) # End of all options + shift + break ;; + -*) + illegalOption "$1" + shift ;; + *) # no more options. Stop while loop + break ;; + esac +done +# DEFAULTS +# set FILE default +if [ "$FILE" != "" ]; then + FILE="$FILE" +else + FILE="$1" +fi +# set OUTPUT_PREFIX default +if [ "$OUTPUT_PREFIX" != "" ]; then + OUTPUT_PREFIX="$OUTPUT_PREFIX" +else + OUTPUT_PREFIX="$DEFAULT_OUTPUT_PREFIX" +fi +# set OUTPUT_SUFFIX default +if [ "$OUTPUT_SUFFIX" != "" ]; then + OUTPUT_SUFFIX="$OUTPUT_SUFFIX" +else + OUTPUT_SUFFIX="$DEFAULT_OUTPUT_SUFFIX" +fi +# set RESIZE default +if [ "$RESIZE" != "" ]; then + RESIZE="$RESIZE" +else + RESIZE="$DEFAULT_RESIZE" +fi +# set IM_ARGS default +if [ "$IM_ARGS" != "" ]; then + IM_ARGS="$IM_ARGS" +else + IM_ARGS="$DEFAULT_IM_ARGS" +fi +# set WMCTRL_TIMEOUT default +if [ "$WMCTRL_TIMEOUT" != "" ]; then + WMCTRL_TIMEOUT="$WMCTRL_TIMEOUT" +else + WMCTRL_TIMEOUT="$DEFAULT_WMCTRL_TIMEOUT" +fi +# set IGNOREDEPENDENCIES +if [[ "$IGNOREDEPENDENCIES" =~ (true|TRUE|YES|yes) ]] ; then + IGNOREDEPENDENCIES=true +else + IGNOREDEPENDENCIES=false +fi + +# requirements +checkRequirements() { + if ! [[ -f "$FILE" ]] ; then + err "No such file: $FILE" + usage + exit 1 + fi + regexresize='^[0-9]*x[0-9]*$' # 1234x4321, but x4132 or 234x must also be possible + if ! [[ "$RESIZE" =~ $regexresize ]] ; then + err "This is not a valid --resize value: $RESIZE" + usage + exit 1 + fi + timeoutnr='^[0-9]+[.]*[0-9]*$' # 0.1 + if ! [[ "$WMCTRL_TIMEOUT" =~ $timeoutnr ]] ; then + err "This is not a valid --wmctrl-timeout value: $WMCTRL_TIMEOUT" + usage + exit 1 + fi + if [[ -n "$PREVIEW" ]]; then + if ! [[ "$PREVIEW" =~ (im|osx|none) ]]; then + err "This is not a valid --preview value: $PREVIEW" + usage + exit 1 + fi + fi + if [[ -n "$BARCODE" ]]; then + if ! [[ "$BARCODE" =~ (dmtx|zbar) ]]; then + err "This is not a valid --barcode value: $BARCODE" + usage + exit 1 + fi + fi +} + +# Dependencies +checkDependencies(){ + DEPS="" + DEPS_FAIL="0" + for DEP in ${DEPENDENCIES[@]}; do + if [[ 0 != "$(command -v "$DEP" >/dev/null ;echo "$?")" ]]; then + err "Missing dependency: $DEP" + DEPS_FAIL=$(($DEPS_FAIL + 1)) + fi + done + if [[ "$DEPS_FAIL" > 0 ]]; then + # only warning when --ignore-dependencies is set. + if [[ "$IGNOREDEPENDENCIES" == "true" ]]; then + err "Ignoring missing dependencies (${DEPS_FAIL}), continue..." + else + err "Fix missing dependencies (${DEPS_FAIL}), exit." + usage + exit 1 + fi + fi +} +# show thumbs per scanner type (-filter box is best for resizing and text) +# layer information can be determined using example files (http://openslide.cs.cmu.edu/download/openslide-testdata/) +getThumb(){ + # get thumb by layer id + LAYER="$LAYER" + convert "$FILEFULL[$LAYER]" -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} +getThumbiScanHT(){ + LAYER=0 + convert "$FILEFULL[$LAYER]" -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} +getThumbAperio(){ + NR_LAYERS=`identify "$FILE" | wc -l` + LAYER="$(($NR_LAYERS - 2))" # the last 2 layers contain thumb and the tissue (couning starts at 0) + convert "$FILEFULL[$LAYER]" -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} +getThumbLeica(){ + LAYER=1 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + convert "$FILEFULL[$LAYER]" -rotate 180 -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} +getThumbHamamatsu(){ + LAYER=7 # determined from example file from our in-house (UMC Utrecht) Hamamatsu scanner + convert "$FILEFULL[$LAYER]" -rotate 90 -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} +getThumbiScan(){ + LAYER=0 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + convert "$FILEFULL[$LAYER]" -filter box -resize "$RESIZE" -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" +} + +programOutput(){ + # set variables + FILE="$FILE" + # path variables + FILEFULL="$(echo "$(cd "$(dirname "$FILE")"; pwd)"/"$(basename "$FILE")")" # full path $FULL + BASENAME="$(basename "$FILEFULL")" # basename + DIRNAME="$(dirname "$FILEFULL")" # dirname + EXTENSION="${BASENAME##*.}" # extension + FILEPATH="${FILEFULL%.*}" # full path, no extension + FILENAME="${BASENAME%.*}" # filename, no extension" + # create tmp working file + PREVIEW_TMPFILE="$(mktemp -q /tmp/${SCRIPTNAME}.XXXXXX)".png + if [ $? -ne 0 ]; then + err "$SCRIPTNAME: Can't create temp file, exiting..." + exit 1 + fi + + # check metadata for scanner data + if [ "$LAYER" != "" ]; then + getThumb # layer is set, do it the easy way + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "iScanHT" | wc -l | awk '{print $1}' )" ]]; then + getThumbiScanHT # iScanHT found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Aperio" | wc -l | awk '{print $1}')" ]]; then + getThumbAperio # Aperio found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Leica" | wc -l | awk '{print $1}')" ]]; then + getThumbLeica # Leica found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Hamamatsu" | wc -l | awk '{print $1}')" ]]; then + getThumbHamamatsu # Hamamatsu found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "iScan" | wc -l | awk '{print $1}')" ]]; then + getThumbiScan # iScan found + else + err "Unknown virtual slide format: $FILE" + err "You probably want to set --layer manually." + err "Run slideInfo for layer information." + usage + exit 1 + fi + + # display preview, im by default or Preview.app if it exists + if [[ -z "$PREVIEW" ]]; then + PREVIEW="im" + if [[ -f "/Applications/Preview.app/Contents/MacOS/Preview" ]]; then + PREVIEW="osx" # OS X Preview.app is available, use it. + fi + fi + + # now the PREVIEW is set with im|osx|none + if [[ "$PREVIEW" == "im" ]];then + # wmctrl makes sure the terminal stays active, and 'display' will open in the background. + wmctrl -T master$$ -r :ACTIVE: ; display "$PREVIEW_TMPFILE" & sleep "$WMCTRL_TIMEOUT" ; wmctrl -a master$$ + IM_DISPLAY_PID=$(ps ax | grep [d]isplay | grep "$(basename "$PREVIEW_TMPFILE")" | awk '{print $1}') + elif [[ "$PREVIEW" == "osx" ]];then + # close preview gently using applescript + open -g -a Preview "$PREVIEW_TMPFILE" + elif [[ "$PREVIEW" == "none" ]];then + PREVIEW="none" # do nothing really. + # echo "Preview is set to none." # tell user/log files what is going on. + fi + + # check barcodes + if [[ "$BARCODE" == "dmtx" ]] ; then + # automatically rename file using datamatrix barcode? + SLIDENAME_BARCODE="$(dmtxread $DEFAULT_DMTXREAD_ARGS $PREVIEW_TMPFILE)" >/dev/null 2>&1 || err "Cancel slide, no barcode found :"$FILEFULL"" + if [[ -z "$SLIDENAME_BARCODE" ]]; then + exit 1 # No barcode found. Exit (Message is already printed) + else + SLIDENAME_UNSAVE="$SLIDENAME_BARCODE" # barcode found, rename + fi + fi + if [[ "$BARCODE" == "zbar" ]] ; then + # automatically rename file using zbar's barcodes? + SLIDENAME_BARCODE="$(zbarimg $DEFAULT_ZBARIMG_ARGS $PREVIEW_TMPFILE)" >/dev/null 2>&1 || err "Cancel slide, no barcode found :"$FILEFULL"" + if [[ -z "$SLIDENAME_BARCODE" ]]; then + exit 1 # No barcode found. Exit (Message is already printed) + else + SLIDENAME_UNSAVE="$SLIDENAME_BARCODE" # barcode found, rename + fi + fi + # ask for filename if non is existing (by typing or by barcode) + while [ "$SLIDENAME_UNSAVE" == "" ]; do + read -p "Enter slide name:" SLIDENAME_UNSAVE + if [[ -z "$SLIDENAME_UNSAVE" ]] ; then + err "Cancel slide..." + exit 1 + fi + done + +# fix this line. +# SLIDENAME_UNSAVE="$(printf "$SLIDENAME_UNSAVE" | strings | perl -p -e 's/^\\[B//g')" # fix strange hand-scanner output + SLIDENAME=$(printf "${OUTPUT_PREFIX}${SLIDENAME_UNSAVE}${OUTPUT_SUFFIX}" | perl -p -e 's/[^A-Za-z0-9._-]/_/g') # Only OS save characters. printf keeps it all on one line + SLIDEFILE="${SLIDENAME}.${EXTENSION}" + # change case if needed + if [[ "CASE_LOWER" = "$CASE" ]]; then + # change case to lower case + SLIDEFILE=$(echo "$SLIDEFILE" | tr [:upper:] [:lower:]) + elif [[ "CASE_UPPER" = "$CASE" ]]; then + # change case to upper case + SLIDEFILE=$(echo "$SLIDEFILE" | tr [:lower:] [:upper:]) + fi + OUTPUTFILE="${DIRNAME}/${SLIDEFILE}" + # rename the file + if [[ -n "$OUTPUTFILE" ]]; then + if [[ ! -a "$OUTPUTFILE" ]]; then + # verbose rename file + mv -v "$FILEFULL" "$OUTPUTFILE" + else + err "Filename exists: $OUTPUTFILE" + exit 1 + fi + else + err "No new filename given." + usage + exit 1 + fi +} + +# all check? +checkRequirements +checkDependencies + +# lets go! +""" +f.write(s) +f.close() +os.chmod(bashfile, 0o755) +bashcmd=bashfile +for arg in sys.argv[1:]: + bashcmd += ' '+arg +subprocess.call(bashcmd, shell=True) diff --git a/python_scribbles/slideServer.py b/python_scribbles/slideServer.py new file mode 100755 index 0000000..059b7b8 --- /dev/null +++ b/python_scribbles/slideServer.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python +# +# deepzoom_server - Example web application for serving whole-slide images +# +# Ref: https://github.com/openslide/openslide-python/blob/master/examples/deepzoom/deepzoom_server.py +# +# Copyright (c) 2010-2015 Carnegie Mellon University +# +# This library is free software; you can redistribute it and/or modify it +# under the terms of version 2.1 of the GNU Lesser General Public License +# as published by the Free Software Foundation. +# +# This library is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY +# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public +# License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this library; if not, write to the Free Software Foundation, +# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +# + +from flask import Flask, abort, make_response, render_template, url_for +from io import BytesIO +import openslide +from openslide import ImageSlide, open_slide +from openslide.deepzoom import DeepZoomGenerator +from optparse import OptionParser +import re +from unicodedata import normalize + +DEEPZOOM_SLIDE = None +DEEPZOOM_FORMAT = 'jpeg' +DEEPZOOM_TILE_SIZE = 254 +DEEPZOOM_OVERLAP = 1 +DEEPZOOM_LIMIT_BOUNDS = True +DEEPZOOM_TILE_QUALITY = 75 +SLIDE_NAME = 'slide' + +app = Flask(__name__) +app.config.from_object(__name__) +app.config.from_envvar('DEEPZOOM_TILER_SETTINGS', silent=True) + + +class PILBytesIO(BytesIO): + def fileno(self): + '''Classic PIL doesn't understand io.UnsupportedOperation.''' + raise AttributeError('Not supported') + + +@app.before_first_request +def load_slide(): + slidefile = app.config['DEEPZOOM_SLIDE'] + if slidefile is None: + raise ValueError('No slide file specified') + config_map = { + 'DEEPZOOM_TILE_SIZE': 'tile_size', + 'DEEPZOOM_OVERLAP': 'overlap', + 'DEEPZOOM_LIMIT_BOUNDS': 'limit_bounds', + } + opts = dict((v, app.config[k]) for k, v in config_map.items()) + slide = open_slide(slidefile) + app.slides = { + SLIDE_NAME: DeepZoomGenerator(slide, **opts) + } + app.associated_images = [] + app.slide_properties = slide.properties + for name, image in slide.associated_images.items(): + app.associated_images.append(name) + slug = slugify(name) + app.slides[slug] = DeepZoomGenerator(ImageSlide(image), **opts) + try: + mpp_x = slide.properties[openslide.PROPERTY_NAME_MPP_X] + mpp_y = slide.properties[openslide.PROPERTY_NAME_MPP_Y] + app.slide_mpp = (float(mpp_x) + float(mpp_y)) / 2 + except (KeyError, ValueError): + app.slide_mpp = 0 + + +@app.route('/') +def index(): + slide_url = url_for('dzi', slug=SLIDE_NAME) + associated_urls = dict((name, url_for('dzi', slug=slugify(name))) + for name in app.associated_images) + return render_template('slide-multipane.html', slide_url=slide_url, + associated=associated_urls, properties=app.slide_properties, + slide_mpp=app.slide_mpp) + + +@app.route('/.dzi') +def dzi(slug): + format = app.config['DEEPZOOM_FORMAT'] + try: + resp = make_response(app.slides[slug].get_dzi(format)) + resp.mimetype = 'application/xml' + return resp + except KeyError: + # Unknown slug + abort(404) + + +@app.route('/_files//_.') +def tile(slug, level, col, row, format): + format = format.lower() + if format != 'jpeg' and format != 'png': + # Not supported by Deep Zoom + abort(404) + try: + tile = app.slides[slug].get_tile(level, (col, row)) + except KeyError: + # Unknown slug + abort(404) + except ValueError: + # Invalid level or coordinates + abort(404) + buf = PILBytesIO() + tile.save(buf, format, quality=app.config['DEEPZOOM_TILE_QUALITY']) + resp = make_response(buf.getvalue()) + resp.mimetype = 'image/%s' % format + return resp + + +def slugify(text): + text = normalize('NFKD', text.lower()).encode('ascii', 'ignore').decode() + return re.sub('[^a-z0-9]+', '-', text) + + +if __name__ == '__main__': + parser = OptionParser(usage='Usage: %prog [options] [slide]') + parser.add_option('-B', '--ignore-bounds', dest='DEEPZOOM_LIMIT_BOUNDS', + default=True, action='store_false', + help='display entire scan area') + parser.add_option('-c', '--config', metavar='FILE', dest='config', + help='config file') + parser.add_option('-d', '--debug', dest='DEBUG', action='store_true', + help='run in debugging mode (insecure)') + parser.add_option('-e', '--overlap', metavar='PIXELS', + dest='DEEPZOOM_OVERLAP', type='int', + help='overlap of adjacent tiles [1]') + parser.add_option('-f', '--format', metavar='{jpeg|png}', + dest='DEEPZOOM_FORMAT', + help='image format for tiles [jpeg]') + parser.add_option('-l', '--listen', metavar='ADDRESS', dest='host', + default='127.0.0.1', + help='address to listen on [127.0.0.1]') + parser.add_option('-p', '--port', metavar='PORT', dest='port', + type='int', default=5000, + help='port to listen on [5000]') + parser.add_option('-Q', '--quality', metavar='QUALITY', + dest='DEEPZOOM_TILE_QUALITY', type='int', + help='JPEG compression quality [75]') + parser.add_option('-s', '--size', metavar='PIXELS', + dest='DEEPZOOM_TILE_SIZE', type='int', + help='tile size [254]') + + (opts, args) = parser.parse_args() + # Load config file if specified + if opts.config is not None: + app.config.from_pyfile(opts.config) + # Overwrite only those settings specified on the command line + for k in dir(opts): + if not k.startswith('_') and getattr(opts, k) is None: + delattr(opts, k) + app.config.from_object(opts) + # Set slide file + try: + app.config['DEEPZOOM_SLIDE'] = args[0] + except IndexError: + if app.config['DEEPZOOM_SLIDE'] is None: + parser.error('No slide file specified') + + app.run(host=opts.host, port=opts.port, threaded=True) \ No newline at end of file diff --git a/python_scribbles/slideTree.py b/python_scribbles/slideTree.py new file mode 100755 index 0000000..5ab6b44 --- /dev/null +++ b/python_scribbles/slideTree.py @@ -0,0 +1,272 @@ +#!/usr/bin/env python +# +# deepzoom_tile - Convert whole-slide images to Deep Zoom format +# +# Ref: https://github.com/openslide/openslide-python/blob/master/examples/deepzoom/deepzoom_tile.py +# +# Copyright (c) 2010-2015 Carnegie Mellon University +# +# This library is free software; you can redistribute it and/or modify it +# under the terms of version 2.1 of the GNU Lesser General Public License +# as published by the Free Software Foundation. +# +# This library is distributed in the hope that it will be useful, but +# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY +# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public +# License for more details. +# +# You should have received a copy of the GNU Lesser General Public License +# along with this library; if not, write to the Free Software Foundation, +# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. +# + +"""An example program to generate a Deep Zoom directory tree from a slide.""" + +from __future__ import print_function +import json +from multiprocessing import Process, JoinableQueue +import openslide +from openslide import open_slide, ImageSlide +from openslide.deepzoom import DeepZoomGenerator +from optparse import OptionParser +import os +import re +import shutil +import sys +from unicodedata import normalize + +VIEWER_SLIDE_NAME = 'slide' + +class TileWorker(Process): + """A child process that generates and writes tiles.""" + + def __init__(self, queue, slidepath, tile_size, overlap, limit_bounds, + quality): + Process.__init__(self, name='TileWorker') + self.daemon = True + self._queue = queue + self._slidepath = slidepath + self._tile_size = tile_size + self._overlap = overlap + self._limit_bounds = limit_bounds + self._quality = quality + self._slide = None + + def run(self): + self._slide = open_slide(self._slidepath) + last_associated = None + dz = self._get_dz() + while True: + data = self._queue.get() + if data is None: + self._queue.task_done() + break + associated, level, address, outfile = data + if last_associated != associated: + dz = self._get_dz(associated) + last_associated = associated + tile = dz.get_tile(level, address) + tile.save(outfile, quality=self._quality) + self._queue.task_done() + + def _get_dz(self, associated=None): + if associated is not None: + image = ImageSlide(self._slide.associated_images[associated]) + else: + image = self._slide + return DeepZoomGenerator(image, self._tile_size, self._overlap, + limit_bounds=self._limit_bounds) + + +class DeepZoomImageTiler(object): + """Handles generation of tiles and metadata for a single image.""" + + def __init__(self, dz, basename, format, associated, queue): + self._dz = dz + self._basename = basename + self._format = format + self._associated = associated + self._queue = queue + self._processed = 0 + + def run(self): + self._write_tiles() + self._write_dzi() + + def _write_tiles(self): + for level in range(self._dz.level_count): + tiledir = os.path.join("%s_files" % self._basename, str(level)) + if not os.path.exists(tiledir): + os.makedirs(tiledir) + cols, rows = self._dz.level_tiles[level] + for row in range(rows): + for col in range(cols): + tilename = os.path.join(tiledir, '%d_%d.%s' % ( + col, row, self._format)) + if not os.path.exists(tilename): + self._queue.put((self._associated, level, (col, row), + tilename)) + self._tile_done() + + def _tile_done(self): + self._processed += 1 + count, total = self._processed, self._dz.tile_count + if count % 100 == 0 or count == total: + print("Tiling %s: wrote %d/%d tiles" % ( + self._associated or 'slide', count, total), + end='\r', file=sys.stderr) + if count == total: + print(file=sys.stderr) + + def _write_dzi(self): + with open('%s.dzi' % self._basename, 'w') as fh: + fh.write(self.get_dzi()) + + def get_dzi(self): + return self._dz.get_dzi(self._format) + + +class DeepZoomStaticTiler(object): + """Handles generation of tiles and metadata for all images in a slide.""" + + def __init__(self, slidepath, basename, format, tile_size, overlap, + limit_bounds, quality, workers, with_viewer): + if with_viewer: + # Check extra dependency before doing a bunch of work + import jinja2 + self._slide = open_slide(slidepath) + self._basename = basename + self._format = format + self._tile_size = tile_size + self._overlap = overlap + self._limit_bounds = limit_bounds + self._queue = JoinableQueue(2 * workers) + self._workers = workers + self._with_viewer = with_viewer + self._dzi_data = {} + for _i in range(workers): + TileWorker(self._queue, slidepath, tile_size, overlap, + limit_bounds, quality).start() + + def run(self): + self._run_image() + if self._with_viewer: + for name in self._slide.associated_images: + self._run_image(name) + self._write_html() + self._write_static() + self._shutdown() + + def _run_image(self, associated=None): + """Run a single image from self._slide.""" + if associated is None: + image = self._slide + if self._with_viewer: + basename = os.path.join(self._basename, VIEWER_SLIDE_NAME) + else: + basename = self._basename + else: + image = ImageSlide(self._slide.associated_images[associated]) + basename = os.path.join(self._basename, self._slugify(associated)) + dz = DeepZoomGenerator(image, self._tile_size, self._overlap, + limit_bounds=self._limit_bounds) + tiler = DeepZoomImageTiler(dz, basename, self._format, associated, + self._queue) + tiler.run() + self._dzi_data[self._url_for(associated)] = tiler.get_dzi() + + def _url_for(self, associated): + if associated is None: + base = VIEWER_SLIDE_NAME + else: + base = self._slugify(associated) + return '%s.dzi' % base + + def _write_html(self): + import jinja2 + env = jinja2.Environment(loader=jinja2.PackageLoader(__name__), + autoescape=True) + template = env.get_template('slide-multipane.html') + associated_urls = dict((n, self._url_for(n)) + for n in self._slide.associated_images) + try: + mpp_x = self._slide.properties[openslide.PROPERTY_NAME_MPP_X] + mpp_y = self._slide.properties[openslide.PROPERTY_NAME_MPP_Y] + mpp = (float(mpp_x) + float(mpp_y)) / 2 + except (KeyError, ValueError): + mpp = 0 + # Embed the dzi metadata in the HTML to work around Chrome's + # refusal to allow XmlHttpRequest from file:///, even when + # the originating page is also a file:/// + data = template.render(slide_url=self._url_for(None), + slide_mpp=mpp, + associated=associated_urls, + properties=self._slide.properties, + dzi_data=json.dumps(self._dzi_data)) + with open(os.path.join(self._basename, 'index.html'), 'w') as fh: + fh.write(data) + + def _write_static(self): + basesrc = os.path.join(os.path.dirname(os.path.abspath(__file__)), + 'static') + basedst = os.path.join(self._basename, 'static') + self._copydir(basesrc, basedst) + self._copydir(os.path.join(basesrc, 'images'), + os.path.join(basedst, 'images')) + + def _copydir(self, src, dest): + if not os.path.exists(dest): + os.makedirs(dest) + for name in os.listdir(src): + srcpath = os.path.join(src, name) + if os.path.isfile(srcpath): + shutil.copy(srcpath, os.path.join(dest, name)) + + @classmethod + def _slugify(cls, text): + text = normalize('NFKD', text.lower()).encode('ascii', 'ignore').decode() + return re.sub('[^a-z0-9]+', '_', text) + + def _shutdown(self): + for _i in range(self._workers): + self._queue.put(None) + self._queue.join() + + +if __name__ == '__main__': + parser = OptionParser(usage='Usage: %prog [options] ') + parser.add_option('-B', '--ignore-bounds', dest='limit_bounds', + default=True, action='store_false', + help='display entire scan area') + parser.add_option('-e', '--overlap', metavar='PIXELS', dest='overlap', + type='int', default=1, + help='overlap of adjacent tiles [1]') + parser.add_option('-f', '--format', metavar='{jpeg|png}', dest='format', + default='jpeg', + help='image format for tiles [jpeg]') + parser.add_option('-j', '--jobs', metavar='COUNT', dest='workers', + type='int', default=4, + help='number of worker processes to start [4]') + parser.add_option('-o', '--output', metavar='NAME', dest='basename', + help='base name of output file') + parser.add_option('-Q', '--quality', metavar='QUALITY', dest='quality', + type='int', default=90, + help='JPEG compression quality [90]') + parser.add_option('-r', '--viewer', dest='with_viewer', + action='store_true', + help='generate directory tree with HTML viewer') + parser.add_option('-s', '--size', metavar='PIXELS', dest='tile_size', + type='int', default=254, + help='tile size [254]') + + (opts, args) = parser.parse_args() + try: + slidepath = args[0] + except IndexError: + parser.error('Missing slide argument') + if opts.basename is None: + opts.basename = os.path.splitext(os.path.basename(slidepath))[0] + + DeepZoomStaticTiler(slidepath, opts.basename, opts.format, + opts.tile_size, opts.overlap, opts.limit_bounds, opts.quality, + opts.workers, opts.with_viewer).run() \ No newline at end of file diff --git a/slide2Tiles b/slide2Tiles index 66b9c36..3794489 100755 --- a/slide2Tiles +++ b/slide2Tiles @@ -3,7 +3,8 @@ # Description: Cut whole virtual slide into tiles # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -38,6 +39,7 @@ DEFAULT_OVERLAP=0 # 0 DEFAULT_OUTPUTDIRSUFFIX=".tiles" DEFAULT_QSUBARGS="-pe threaded 4 -q veryshort" MINBYTESIZE=100000 # tissue detection is simple, this is the min size in bytes of the output .png for tissue to be present +# MINBYTESIZE=1000 # tissue detection is simple, this is the min size in bytes of the output .png for tissue to be present # sort arrays DEPENDENCIES=( $( for el in "${DEPENDENCIES[@]}"; do echo "$el"; done | sort) ) SUPPORTED_FORMATS=( $( for el in "${SUPPORTED_FORMATS[@]}"; do echo "$el"; done | sort) ) @@ -65,11 +67,12 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) -Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +UMC Utrecht, Utrecht, the Netherlands. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the @@ -85,13 +88,14 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | http://github.com/swvanderlaan. + EOF } # help message helpMessage() { -verbose_debug "helpMessage()" cat <<- EOF ${SCRIPTNAME}: ${DESCRIPTIONSHORT} @@ -139,19 +143,23 @@ supported formats: ${SUPPORTED_FORMATS[@]} Cut slides into tiles for further processing. Reasonable tile size is '$DEFAULT_WIDTH' by '$DEFAULT_HEIGHT' pixels (default). Tiles are stored in a folder with suffix '$DEFAULT_OUTPUTDIRSUFFIX' at the same location -as the input file. By default all empty tiles are deleted. A -memory-mapped disk file (image-magick .mpc file) is used when a mask is +as the input file. By default all empty tiles are deleted. +A memory-mapped disk file (image-magick .mpc file) is used when a mask is used. The fast and efficient 'openslide-write-png' is used when no mask -is used. Pipe the 'job list' output in 'parallel' for parallel -processing. For some slides, the virtual slide format is not known. For -these instances you need to run slideInfo to find the correct layer -manually. Use the --layer flag to set the correct layer. The amount ---overlap is only affective on one side, the left tile overlaps the tile -on the right, and the top tile overlaps the tile below. +is used. +Pipe the 'job list' output in 'parallel' for parallel processing. +For some slides, the virtual slide format is not known. For these instances +you need to run slideInfo to find the correct layer manually. Use the --layer +flag to set the correct layer. +The amount --overlap is only effective on one side, the left tile overlaps +the tile on the right, and the top tile overlaps the tile below. + +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } @@ -414,9 +422,21 @@ verbose_debug "createTilesConvert()" verbose_debug "Virtual slide: $S" dimensionsslide="$(identify -format %w "${S}"["${LAYER}"])"x"$(identify -format %h "${S}"["${LAYER}"])" verbose_debug "Create buffer [$dimensionsslide]; $buffer" + date1="$(date +'%s')" # convert -limit area "${MEMORYLIMIT}" "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" - convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" + # convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" + + # 20211108: There was an update to convert, having to do with the difference + # between IM6 and IM7 causing a (weird) behaviour where the non-masked area is + # completely whitened. + # Obviously we do not want this, this cause the tiling to go all wonky. + # Reference: https://imagemagick.org/script/porting.php + # IF this script is causing problems, it's probably due to this difference. + # THEORETICALLY slideToolKit in combination with CellProfiler 2.2.0, Java 8, Xcode, + # and homebrew should work out-of-the-box without `-channel RGB -negate`. No guarantee though. + # + convert "${S}[${LAYER}]" \( "${M}" -fuzz 99% -transparent white -scale ${dimensionsslide} -channel RGB -negate \) -composite -fuzz 3% -trim +repage -bordercolor white -border 30x30 +repage "${buffer}" date2="$(date +'%s')" diff="$(($date2-$date1))" @@ -450,6 +470,8 @@ verbose_debug "createTilesConvert()" h="$(( y * $TILESHEIGHT ))" FILE="${OUTPUTDIRNAME}/${tile}" filename="${FILE%.*}" # full path without extension + + verbose_debug "Tiling image [$tile]: tile size [${TILESWIDTH_OVERLAP}x${TILESHEIGHT_OVERLAP}+$w+$h]" convert "$buffer" -crop "${TILESWIDTH_OVERLAP}x${TILESHEIGHT_OVERLAP}+$w+$h" +repage "$FILE" # find tissue, needs improvement if [[ "$(wc -c < "$FILE" )" -ge "$MINBYTESIZE" ]]; then @@ -461,6 +483,7 @@ verbose_debug "createTilesConvert()" # no tissue found TISSUE="empty"; if [ "$KEEPEMPTYTILES" == "true" ]; then + echo "keeping empty tiles" mv "$FILE" "${filename}.${TISSUE}.png" else rm -rf "$FILE" @@ -528,6 +551,7 @@ verbose_debug "createTilesOpenslide()" # no tissue found TISSUE="empty"; if [ "$KEEPEMPTYTILES" == "true" ]; then + echo "keeping empty tiles" mv "$FILE" "${filename}"".""${TISSUE}.png" else rm -rf "$FILE" @@ -553,4 +577,4 @@ checkRequirements checkDependencies # lets go! -programOutput \ No newline at end of file +programOutput diff --git a/slideCompare.sh b/slideCompare.sh new file mode 100755 index 0000000..d1f00c3 --- /dev/null +++ b/slideCompare.sh @@ -0,0 +1,309 @@ +#!/bin/bash + +# +# Description: Compare two images with ImageMagick's compare function. +# +# https://softwarerecs.stackexchange.com/questions/9774/command-line-tool-to-check-whether-two-images-are-exactly-the-same-graphically + +# Description: +# This script starts up the slideToolKit-CP4 Anaconda3 environment. Next it will extract +# slide-macro images from each .ndpi-file present in a given directory. Lastly it will +# take a list of studynumbers/slidenumbers to + +# +# The MIT License (MIT) +# Copyright (c) 2022, Sander W. van der Laan, UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2022-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- slidenumber with stain; only 1 slidenumber is permitted, for example, AE4653.CD3." + echoerror " Two images are expected, for example AE4653.CD3.ndpi and AE4653.CD3.v2.npi - the " + echoerror " script expects 'v2' as the indication for the duplicate. At the moment only " + echoerror " duplicates can be checked, not multiplicates." + echoerror "" + echoerror "An example command would be: slideCompare [arg1: AE4653.CD3]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideCompare: compare images" +echo "" +echoitalic "* Written by : Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2022-08-16" +echoitalic "* Version : 1.0.0-beta" +echo "" +echoitalic "* Description : This script will compare two images with ImageMagick's compare " +echoitalic " function." +echoitalic " This is SLURM based." +echoitalic " " +echoitalic " Note this is a beta-version. There are still some fixes to make." +echoitalic " These are the MoSCoW wishes (M[ust]/S[hould]/C[ould]/W[ould])" +echoitalic " - logging needs improvement (now there are many duplicate lines in the code) (S)" +echoitalic " - it does not work for .tif and .TIF (M)" +echoitalic " - it expects duplicates, should work for multiplicates too (C)" +echoitalic " - it expects slidenumbers with stain (AE4653.CD3) it should work with two arguments (1 for slidenumber, 1 for stain) (M)" +echoitalic " - it expects a certain file format (e.g. AE4653.CD3.ndpi and AE4653.CD3.v2.npi), it should be more automatic and just search on the slidenumber-stain combination (M)" +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +IMG_FILE="$1" # Depends on arg1 + +### Creating a log file for quick review. +### We use touch: if it already exist, it will not be re-created. +touch slideCompare.logfile.txt +# echo "### slideCompare LogFile" >> slideCompare.logfile.txt +# echo "" >> slideCompare.logfile.txt +# echo "This LogFile contains the relevant information regarding converting and comparing images." >> slideCompare.logfile.txt +# echo "" >> slideCompare.logfile.txt + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideCompare ***!" + + ### adding to log + script_arguments_error >> slideCompare.logfile.txt + echo "You must supply correct (number of) arguments when running *** slideCompare ***!" >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + +else + + # Reference + # https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script + SECONDS=0 + # do some work + + # checking if masks exist - if so, skip this script + if [ -f ${IMG_FILE}.diff.png ]; then + echo "..... > This image was already compared as the [ ${IMG_FILE}.diff.png] already exists - moving on." + + ### adding to log + echo "Comparing images for slidenumber: ${IMG_FILE}." >> slideCompare.logfile.txt + echo "..... > This image was already compared as the [ ${IMG_FILE}.diff.png] already exists - moving on." >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + script_copyright_message + + exit 1 + fi + + ### Loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > Loading required anaconda environment containing the slideToolKit-CellProfiler4.1.3 installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + SLIDEMACRO="/hpc/local/CentOS7/dhl_ec/software/slideToolKit/slideMacro.py" + + echo "..... > Creating macro-images from original image-file and comparing macro-images." + + ### DEBUG + ### ls -lh $IMG_FILE.*ndpi + + if [[ ${IMG_FILE}.*ndpi ]]; then + echo "The image-file is a NDPI and small macro images will be created for comparing." + echo "" + + ### adding to log + echo "Comparing images for slidenumber: ${IMG_FILE}." >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + echo "* creating macro-files." + + python $SLIDEMACRO --input ${IMG_FILE}.*ndpi --level 7 --verbose + + # How to get this (example below) information from the python script in the log? + # Is it even relevant? +# Processing [ AE4725.CD3.ndpi ] at level [ 7 ]. +# * image dimensions (height x width in pixels): (270, 330, 3) +# * image size: 261.04 KB +# Processing [ AE4725.CD3.v2.ndpi ] at level [ 7 ]. +# * image dimensions (height x width in pixels): (270, 330, 3) +# * image size: 261.04 KB + + + echo "" + echo "* comparing macro-images." + + compare_result=$(magick compare -metric AE ${IMG_FILE}.macro.png ${IMG_FILE}.v2.macro.png ${IMG_FILE}.diff.png 2>&1); + + if [ "${compare_result}" != '0' ]; then + echo "ERROR: ImageMagick determined [ ${compare_result} ] incorrect pixels in slide ${IMG_FILE}. These images are not the same. Please manually inspect the original images (e.g. in .ndpi-format)."; + + ### adding to log + echo "ERROR: ImageMagick determined [ ${compare_result} ] incorrect pixels in slide ${IMG_FILE}. These images are not the same. Please manually inspect the original images (e.g. in .ndpi-format)." >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + else + echo "NO DIFFERENCE: ImageMagick determined [ ${compare_result} ] incorrect pixels in slide ${IMG_FILE}. These images are the same; as are the original uncoverted images. "; + ### adding to log + echo "NO DIFFERENCE: ImageMagick determined [ ${compare_result} ] incorrect pixels in slide ${IMG_FILE}. These images are the same; as are the original uncoverted images. " >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + fi + + elif [[ ${IMG_FILE}.*tif ]]; then + echo "The image-file is a .tif. AT THE MOMENT ONLY WORKS FOR NDPI - EXITING" + + ### adding to log + echo "The image-file is a .tif. AT THE MOMENT ONLY WORKS FOR NDPI - EXITING" >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + script_copyright_message + + exit 1 + + elif [[ ${IMG_FILE}.*TIF ]]; then + echo "The image-file is a .TIF. AT THE MOMENT ONLY WORKS FOR NDPI - EXITING" + + ### adding to log + echo "The image-file is a .TIF. AT THE MOMENT ONLY WORKS FOR NDPI - EXITING" >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + script_copyright_message + + exit 1 + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF'] + AT THE MOMENT ONLY WORKS FOR NDPI" + + ### adding to log + echo "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF'] + AT THE MOMENT ONLY WORKS FOR NDPI" >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + + script_copyright_message + + exit 1 + + fi + + echo "" + echo "..... Comparing successfully finished." + + duration=$SECONDS + echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + compare_time="[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + + ### adding to log + echo "Comparison took: $compare_time" >> slideCompare.logfile.txt + echo "" >> slideCompare.logfile.txt + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideConvert b/slideConvert index 7796df6..925cbfd 100755 --- a/slideConvert +++ b/slideConvert @@ -3,7 +3,8 @@ # Description: Convert digital slide format into Generic-TIFF # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -49,11 +50,12 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) -Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +UMC Utrecht, Utrecht, the Netherlands. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the @@ -69,6 +71,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -99,13 +103,16 @@ print a job list: dependencies: ${DEPENDENCIES[@]} Convert slides of multiple formats into OME-TIFF format which can be -used by the slideToolkit. By default 'bfconvert' runs without selective +used by the slideToolKit. By default 'bfconvert' runs without selective arguments, you can set arguments using the --bfconvert-args flag. See 'bfconvert' for more information. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } @@ -221,4 +228,4 @@ checkRequirements checkDependencies # lets go! -programOutput \ No newline at end of file +programOutput diff --git a/slideDirectory b/slideDirectory index ed08f70..9de0e2b 100755 --- a/slideDirectory +++ b/slideDirectory @@ -3,7 +3,8 @@ # Description: Create a staging directorie per slide # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -45,11 +46,13 @@ EOF # version info version() { cat << EOF -slideToolkit 0.2 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) -Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +Copyright (c) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, +UMC Utrecht, the Netherlands. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the @@ -65,6 +68,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -99,9 +104,12 @@ dependencies: ${DEPENDENCIES[@]} Move file into a similar named directory at the same location. Pipe the 'job list' output in 'parallel' for parallel processing. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } diff --git a/slideExtract.py b/slideExtract.py new file mode 100755 index 0000000..42e3773 --- /dev/null +++ b/slideExtract.py @@ -0,0 +1,118 @@ +#!/usr/bin/env python +# script to extract a thumbnails and macros whole-slide image files (*.TIF, *.NDPI, etc.) +# +# Ref: https://github.com/choosehappy/Snippets/blob/master/extract_macro_level_from_wsi_image_openslide_cli.py +# + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideExtract: extract thumbnails and macro-images ") +print("") +print("* Version : v1.0.3") +print("") +print("* Last update : 2021-11-18") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Inspired by : choosehappy | https://github.com/choosehappy") +print("") +print("* Description : This script will get thumbnails and macro images from (a list of given) images ") +print(" for quick inspection.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import numpy as np +import glob +from pathlib import Path + +# for argument parser +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +parser = argparse.ArgumentParser( + prog='slideExtract', + description='This script will get thumbnails and macro images from given images for quick inspection.', + usage='slideExtract -i/--input -l/--levels; optional: -d/--display -o/--outdir -s/--suffix -t/--type -f/--force -v/--verbose; for help: -h/--help', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) + +parser.add_argument('-o', '--outdir', help="Output dir, default is present working directory.", default="./", type=str) +parser.add_argument('-s', '--suffix', help="Suffix to append to end of file, default is 'm' for thumbnail and '#' for a given level.", default="", type=str) +parser.add_argument('-t', '--type', help="Output file type, default is png (which is slower), other options are tif.", default="png", type=str) +parser.add_argument('-f', '--force', help="Force output even if it exists.", default=False, action="store_true") +parser.add_argument('-v', '--verbose', help="While writing images also display image properties.", default=False, action="store_true") + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument('-i','--input',help="Input (directory containing files). Try: *.TIF or /path_to/images/*.ndpi.", nargs="*") +requiredNamed.add_argument('-l','--levels',help="Comma seperated list of magnification levels to extract, with 'm' as thumbnail and '6' as level 6. Try: m,6.") + +args = parser.parse_args() + +if not args.input: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideExtract ***!") + print("Note that -i/--input is required. Try: *.TIF or /path_to/images/*.ndpi.\n") + parser.print_help() + exit() + +if len(args.input) > 1: # bash has sent us a list of files + files = args.input +else: # user sent us a wildcard, need to use glob to find files + files = glob.glob(args.input[0]) + +if not os.path.exists(args.outdir): + os.makedirs(args.outdir) + +for fname in files: + fnameout=f"{Path(fname).stem}{args.suffix}.!!.{args.type if args.type[0] == '' else ''+args.type}" + fnameout=Path(args.outdir,fnameout) + + if not args.force and os.path.exists(fnameout): + print(f"Skipping {fnameout} as output file exists and --force is not set") + continue + + fimage=openslide.OpenSlide(fname) + + for level in args.levels.split(","): + if level == 'm': + img = fimage.associated_images["macro"] + else: + level = int(level) + img = fimage.read_region((0, 0), level, fimage.level_dimensions[level]) + img = np.asarray(img)[:,:, 0:3] + + if args.verbose: + print("Processing [",fname,"] at level [",level,"].") + print('* image dimensions (height x width in pixels):', img.shape) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + cv2.imwrite(str(fnameout).replace("!!",str(level)),cv2.cvtColor(img,cv2.COLOR_RGB2BGR)) + else: + print("Writing images for [",fname,"] at level [",level,"].") + cv2.imwrite(str(fnameout).replace("!!",str(level)),cv2.cvtColor(img,cv2.COLOR_RGB2BGR)) + +# print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +# print("+ The MIT License (MIT) +") +# print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +# print("+ +") +# print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +# print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +# print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +# print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +# print("+ following conditions: +") +# print("+ +") +# print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +# print("+ portions of the Software. +") +# print("+ +") +# print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +# print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +# print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +# print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +# print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +# print("+ +") +# print("+ Reference: http://opensource.org. +") +# print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/slideInfo b/slideInfo index d8fdeee..1ee0c18 100755 --- a/slideInfo +++ b/slideInfo @@ -3,7 +3,8 @@ # Description: Fetch slide metadata (resolution, dates, etc) # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -47,8 +48,8 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. @@ -67,6 +68,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -97,9 +100,12 @@ Show virtual slide metadata/information: color depth, layers, etc. CAVE: $SCRIPTNAME does suppress lots of warnings! -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } diff --git a/slideInfo.py b/slideInfo.py new file mode 100755 index 0000000..e6499cb --- /dev/null +++ b/slideInfo.py @@ -0,0 +1,123 @@ +#!/usr/bin/python +# +# Ref: https://lists.andrew.cmu.edu/pipermail/openslide-users/2015-May/001060.html +# +### ADD-IN: +### - requirement check +### - if not present install +### - report that the requirements are met +### - get in level count +# +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideInfo: whole-slide image information ") +print("") +print("* Version : v1.0.2") +print("") +print("* Last update : 2021-08-31") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Suggested for by : Toby Cornish") +print("") +print("* Description : This script will get whole-slide image information from (a list of given) images ") +print(" for quick inspection.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import numpy as np +import sys +import glob +from pathlib import Path + +# for argument parser +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +parser = argparse.ArgumentParser( + prog='slideNDPInfo', + description='This script will get whole-slide image information from (a list of given) images for quick inspection.', + usage='slideNDPInfo [-h/--help] -i/--input.', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) +parser.add_argument('-v', '--verbose', help="Will get all available image properties.", default=False, action="store_true") + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument('-i','--input',help="Input (directory containing files). Try: AE107.HE.ndpi or AE-SLIDES/HE/*.ndpi.", nargs="*") + +args = parser.parse_args() + +if not args.input: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideMacro ***!") + print("Note that -i/--input is required. Try: AE107.HE.ndpi or AE-SLIDES/HE/*.ndpi.\n") + parser.print_help() + exit() + +if len(args.input) > 1: # bash has sent us a list of files + files = args.input +else: # user sent us a wildcard, need to use glob to find files + files = glob.glob(args.input[0]) + +# globals + +print('Python version: ',sys.version) +print('OpenSlide version: ',openslide.__version__) +print('OpenSlide library version: ', openslide.__library_version__) + + +for fname in files: + if args.verbose: + slide=openslide.OpenSlide(fname) + print("Showing all image properties for [",fname,"]:") + for prop_key in slide.properties.keys(): + print(" Property: " + str(prop_key) + ", value: " + str(slide.properties.get(prop_key))) + + else: + slide=openslide.OpenSlide(fname) + print("Showing some properties for slide [",fname,"] with format: " + str(slide.detect_format(fname))) + print("Slide associated images:") + for ai_key in slide.associated_images.keys(): + print(" " + str(ai_key) + ": " + str(slide.associated_images.get(ai_key))) + + macroIm = slide.associated_images['macro'] + img = np.asarray(macroIm)[:,:, 0:3] + img_size = img.size/1024 # to get kilobytes + print('* key-image dimensions (height x width in pixels):', img.shape) + print('* key-image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + print("Slide dimensions: " + str(slide.dimensions)) + objective_power = int(slide.properties[openslide.PROPERTY_NAME_OBJECTIVE_POWER]) + print("Slide objective power: " + str(objective_power)) + + print('Available \'levels\': ',slide.level_count) + print("* level count: %d" % slide.level_count) + print("* level dimensions: " + str(slide.level_dimensions)) + print("* level downsamples: " + str(slide.level_downsamples)) + + slide.close() + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/slideJobChecker b/slideJobChecker new file mode 100755 index 0000000..797512b --- /dev/null +++ b/slideJobChecker @@ -0,0 +1,228 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +function echosucces { + echo -e "${YELLOW}${1}${NONE}" +} +function importantnote { + echo -e "${CYAN}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #3 -- analysis step to be checked, i.e. should contain the start of the error file: [slidemask, slide2tiles, slidenormalize, slidecellprofiler, slidewrapup]." + echoerror "- Argument #4 -- Unique label. A string-number combination (no spaces) for reference to the analysis run. [OPTIONAL]" + echoerror "" + echoerror "An example command would be: slideJobChecker [arg1: STAIN] [arg2: path_to_working_directory] [arg3: analysis step]" + echoerror "" + echoerror "Please note that this command should be run within the working directory (Argument #2)." + echoerror "" + echoerror "NOTE: Currently only works for slide2tiles-error-files. Should be expanded to other error-files too." + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideJobChecker" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-18" +echoitalic "* Version : 1.0.1" +echo "" +echoitalic "* Description : This script will check the contents of error files for a " +echoitalic " given project directory. " +# echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +PROJECTDIR="$2" # Depends on arg2 +STEP="$3" + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +UNIQUE_LABEL=${4-V1} # Depends on arg9 +# BATCH_SIZE=${10-5000} # Depends on arg10 +# RANDOM_SAMPLE=${11-50} # Depends on arg11 + +QJOB_MEM_SLIDEMASK="100G" +QJOB_TIME_SLIDEMASK="02:30:00" + +QJOB_MEM_SLIDE2TILES="150G" +QJOB_TIME_SLIDE2TILES="04:30:00" + +QJOB_MEM_SLIDENORM="5G" +QJOB_TIME_SLIDENORM="02:00:00" + +QJOB_MEM_CP="12G" +QJOB_TIME_CP="12:00:00" + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 3 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideJobChecker ***!" + +else + + echo "" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "Checking consistency of slideToolKit run." + + ### Creating necessary readme + echo "* Making necessary 'readme' files..." + echo "Slide_num Filename N_Images_Analyzed Message" > ${PROJECTDIR}/readme.${STEP}.${STAIN}.${UNIQUE_LABEL} + + ### Setting the patterns to look for -- never change this + ERROR_PATTERN="slurmstepd: error:" + IMAGESANALYZED_PATTERN="module Images" # only in cellprofiler error file + + echo "Checking errors-files and zapping them if empty..." + for ERRORFILE in $(ls $PROJECTDIR/errors/${STEP}.*.errors); do + + if [[ ! -z $(grep "${ERROR_PATTERN}" "${ERRORFILE}") ]] ; then + + ERRORFILENAME=$(basename $ERRORFILE ) + SLIDE_NUM=$(basename "$ERRORFILE") | cut -d'.' -f2 + ERROR_MESSAGE=$(grep "${ERROR_PATTERN}" "${ERRORFILE}") + + N_IMAGES_ANALYZED=$(grep "${IMAGESANALYZED_PATTERN}" "${ERRORFILE}" | wc -l) + + echoerrorflash "*** ERROR *** The error-file [ $ERRORFILENAME ] contains an error. We'll keep it there for review and check the contents." + echoerror "> number of images analyzed................: ${N_IMAGES_ANALYZED}" +# echoerror "> error message............................: ${ERROR_MESSAGE}" + echoerror "" + echo "${SLIDE_NUM} ${ERRORFILENAME} ${ERROR_MESSAGE} " >> ${PROJECTDIR}/readme.${STEP}.${STAIN}.${UNIQUE_LABEL} + + else + + ERRORFILENAME=$(basename $ERRORFILE ) + SLIDE_NUM=$(basename "$ERRORFILE") | cut -d'.' -f2 + + N_IMAGES_ANALYZED=$(grep "${IMAGESANALYZED_PATTERN}" "${ERRORFILE}" | wc -l) + + SUCCESSMESSAGE=$(echosucces "successfully analyzed") + SUCCESSMESSAGEREADME=$(echo "success") + + echosucces "*** SUCCESS *** Step ${STEP} was succefull for slide ${SLIDE_NUM}..." + echosucces "Reported in the [ ${ERRORFILENAME} ]: " + echosucces "####################################################################################" + head ${ERRORFILE} + tail ${ERRORFILE} + echosucces "####################################################################################" + + echosucces "> number of images analyzed..................: ${N_IMAGES_ANALYZED}" +# echosucces "> success message............................: ${SUCCESSMESSAGE}" + echosucces "" + echo "${SLIDE_NUM} ${ERRORFILENAME} ${SUCCESSMESSAGEREADME} " >> ${PROJECTDIR}/readme.${STEP}.${STAIN}.${UNIQUE_LABEL} + + BASENAME_ERRORFILE=$(basename ERRORFILENAME .errors) + +# echo "Removing files [ ${PROJECTDIR}/[errors/logs]/[.errors/.log] ]..." +# rm -v ${PROJECTDIR}/errors/${ERRORFILENAME} +# rm -v ${PROJECTDIR}/logs/${BASENAME_ERRORFILE}.log + + ### END of if-else statement + fi + + ### END of for-loop + done + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideJobsCellProfiler b/slideJobsCellProfiler index 26fce9b..b26c4d7 100755 --- a/slideJobsCellProfiler +++ b/slideJobsCellProfiler @@ -3,7 +3,8 @@ # Description: Create a joblist to analyse multiple tiles using CellProfiler. # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -64,8 +65,8 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. @@ -84,6 +85,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -131,9 +134,12 @@ starting the pipeline. ${SCRIPTNAME} expects "$CPBIN" to exist as a script that redirects to your CellProfiler binairy. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } diff --git a/slideMacro b/slideMacro new file mode 100755 index 0000000..f5fdc96 --- /dev/null +++ b/slideMacro @@ -0,0 +1,325 @@ +#!/bin/bash +# +# Description: Create scaled mask and macro from a virtual slide +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +# Variables +SCRIPTNAME=$(basename $0) +DESCRIPTIONSHORT="Create *.png from a virtual slide." +DEPENDENCIES=("convert") +SUPPORTED_FORMATS=("TIFF" "Aperio" "Hamamatsu" "iScan" "iScanHT" "Leica") +DEFAULT_MACROSUFFIX=".macro" +DEFAULT_MACRO_THUMBSIZE="" #"2000x2000" # Maximum size of macro image +DEFAULT_IM_ARGS="" # convert arguments # -verbose +DEFAULT_SLURMARGS="--time 00:05:00 --mem=8G" # Maximum values: rt=runtime HH:MM:SS vmem=GB + +# Errors go to stderr +err() { + echo "${SCRIPTNAME} ERROR: $@" >&2 +} + +# usage message +usage() { +cat <<- EOF +$SCRIPTNAME --help for more information. +EOF +} + +# version info +version() { +cat << EOF +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) + +The MIT License (MIT) +Copyright (c) 2014-2020, Bas G.L. Nelissen & Sander W. van der Laan, UMC Utrecht, Utrecht, the Netherlands. + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the +"Software"), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be included +in all copies or substantial portions of the Software. + +There is NO WARRANTY, to the extent permitted by law. + +Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + +EOF +} + + +# help message +helpMessage() { +cat <<- EOF +${SCRIPTNAME}: ${DESCRIPTIONSHORT} + +usage: + $SCRIPTNAME [options] -f + +options: + -f, --file virtual slide to create mask from + + [--macrosuffix] macro suffix ["$DEFAULT_MACROSUFFIX"] + [-l, --layer] force specific layer by layer id + [--convert-args]='' convert arguments ('=' sign and 'quotes' required) + [--ignore-dependencies] ignores all dependencies, but gives warning + + --help display this help + --version display version and license information + + +examples: + $SCRIPTNAME "file.TIF" + $SCRIPTNAME --file="file.TIF" + $SCRIPTNAME -f "file.TIF" + $SCRIPTNAME -f "file.TIF --macrosuffix=".layer5" + +print a job list: + find "\$(pwd)" -iname "*.TIF" -printf 'slideMacro is processing file [ %p ].\n' -exec $(command -v "$SCRIPTNAME") --file "{}" \; + +print a job list for 'slurm': + find "\$(pwd)" -iname "*.TIF" -type f \\ + -exec printf "sbatch $DEFAULT_SLURMARGS --chdir="\$(pwd)/" " \; \\ + -exec printf "\$(command -v "$SCRIPTNAME") " \; \\ + -exec echo "--file=\""{}"\" " \; + +dependencies: ${DEPENDENCIES[@]} +supported formats: ${SUPPORTED_FORMATS[@]} + +You can extract a layer to quickly inspect the image. This program will +use 'convert' to create a '*.macro.png'-file for you which you can +inspect. The 'macro'-image is a layer within the original TIF file. +Pipe the 'multiple folders at once' output in 'parallel' for parallel +processing. +For some slides, the virtual slide format is not known. For these +instances you need to run slideInfo to find the correct layer manually. +When the layer is identified (approximately 2000x2000 pixels), use +the --layer flag. + +NOTE #1: by default we set -quiet to remove warnings such as those described +here: https://stackoverflow.com/questions/27608124/imagemagick-how-to-get-rid-of-tiffwarnings-768-message-about-unknown-field-wh + +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. + +EOF +} +# Menu +# Empty variables +FILE="" +LAYER="" +MACROSUFFIX="" +# illegal option +illegalOption() { +cat <<- EOF +$SCRIPTNAME: illegal option $1 +$(usage) +EOF +exit 1 +} +# loop through options +while : +do + case $1 in + -h) + usage + exit 0 ;; + --help | -\?) + helpMessage + exit 0 ;; + --version ) + version + exit 0 ;; + -f | --file) + FILE=$2 + shift 2 ;; + --file=*) + FILE=${1#*=} + shift ;; + -l | --layer) + LAYER=$2 + shift 2 ;; + --layer=*) + LAYER=${1#*=} + shift ;; + --macrosuffix) + MACROSUFFIX=$2 + shift 2 ;; + --macrosuffix=*) + MACROSUFFIX=${1#*=} + shift ;; + --convert-args=*) # Equal sign and quotes are required for this option. If not, the convert arguments are seen as slideRename options. + IM_ARGS=${1#*=} + shift ;; + --ignore-dependencies) + IGNOREDEPENDENCIES=TRUE + shift ;; + --) # End of all options + shift + break ;; + -*) + illegalOption "$1" + shift ;; + *) # no more options. Stop while loop + break ;; + esac +done +# DEFAULTS +# set FILE +if [ "$FILE" != "" ]; then + FILE="$FILE" +else + FILE="$1" +fi +# set MACROSUFFIX default +if [ "$MACROSUFFIX" != "$DEFAULT_MACROSUFFIX" ]; then + MACROSUFFIX="$MACROSUFFIX" +else + MACROSUFFIX="$DEFAULT_MACROSUFFIX" +fi +# set IM_ARGS default +if [ "$IM_ARGS" != "" ]; then + IM_ARGS="$IM_ARGS" +else + IM_ARGS="$DEFAULT_IM_ARGS" +fi +# set IGNOREDEPENDENCIES +if [[ "$IGNOREDEPENDENCIES" =~ (true|TRUE|YES|yes) ]] ; then + IGNOREDEPENDENCIES=true +else + IGNOREDEPENDENCIES=false +fi + +# requirements +checkRequirements() { + if ! [[ -f "$FILE" ]] ; then + err "No such file: $FILE" + usage + exit 1 + fi +} + +# Dependencies +checkDependencies(){ + DEPS="" + DEPS_FAIL="0" + for DEP in ${DEPENDENCIES[@]}; do + if [[ 0 != "$(command -v "$DEP" >/dev/null ;echo "$?")" ]]; then + err "Missing dependency: $DEP" + DEPS_FAIL=$(($DEPS_FAIL + 1)) + fi + done + if [[ "$DEPS_FAIL" > 0 ]]; then + # only warning when --ignore-dependencies is set. + if [[ "$IGNOREDEPENDENCIES" == "true" ]]; then + err "Ignoring missing dependencies (${DEPS_FAIL}), continue..." + else + err "Fix missing dependencies (${DEPS_FAIL}), exit." + usage + exit 1 + fi + fi +} +# get mask function for each type of scanner +getMacro(){ + # get thumb by layer id + LAYER="$LAYER" + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" +} +getMacroiScanHT(){ + # layer 8 contains the macro + LAYER=8 + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" +} +getMacroAperio(){ + # the third to last layer contains the macro + NR_LAYERS=`identify "$FILE" | wc -l` + LAYER="$(($NR_LAYERS - 3))" + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" +} +getMacroLeica(){ + LAYER=5 # 5 determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" +} +getMacroHamamatsu(){ + LAYER=5 # 5 determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + # convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% "$MACRO" +} +getMacroiScan(){ + LAYER=7 # 7 determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + convert -quiet $IM_ARGS "$FILEFULL[$LAYER]" -resize "${DEFAULT_MACRO_THUMBSIZE}>" -fuzz 1% -transparent "#ECECEC" "$MACRO" +} + +# actual program +programOutput(){ + # set variables + LAYER="$LAYER" + FILEFULL="$(echo "$(cd "$(dirname "$FILE")"; pwd)"/"$(basename "$FILE")")" # full path $FULL + BASENAME="$(basename "$FILEFULL")" # basename + EXTENSION="${BASENAME##*.}" # extension + FILEPATH="${FILEFULL%.*}" # full path, no extension + FILENAME="${BASENAME%.*}" # filename, no extension" + MACRO="${FILEPATH}${MACROSUFFIX}.png" + + # check metadata for scanner data + if [ "$LAYER" != "" ]; then + getMacro # layer is set, do it the easy way + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "iScanHT" | wc -l | awk '{print $1}' )" ]]; then + getMacroiScanHT # iScanHT found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Aperio" | wc -l | awk '{print $1}')" ]]; then + getMacroAperio # Aperio found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Leica" | wc -l | awk '{print $1}')" ]]; then + getMacroLeica # Leica found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "Hamamatsu" | wc -l | awk '{print $1}')" ]]; then + getMacroHamamatsu # Hamamatsu found + elif [[ "0" < "$(tiffinfo "$FILEFULL" 2>&1 | strings | grep -i "iScan" | wc -l | awk '{print $1}')" ]]; then + getMacroiScan # Ventana iScan found + else err "Unknown virtual slide format: $FILEFULL" + err "You probably want to set --layer manually." + err "Run slideInfo for layer information." + usage + exit 1 + fi + +} +# all check? +checkRequirements +checkDependencies + +# lets go! +# actual program +programOutput diff --git a/slideMacro.py b/slideMacro.py new file mode 100755 index 0000000..9cd9e84 --- /dev/null +++ b/slideMacro.py @@ -0,0 +1,147 @@ +#!/usr/bin/env python +# script to extract a macro images (and display) whole-slide image files (*.TIF, *.NDPI, etc.) +# +# Ref: https://github.com/choosehappy/Snippets/blob/master/extract_macro_level_from_wsi_image_openslide_cli.py +# + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideMacro: extract macro images ") +print("") +print("* Version : v1.0.2") +print("") +print("* Last update : 2022-08-05") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Inspired by : choosehappy | https://github.com/choosehappy") +print("") +print("* Description : This script will get macro-images at a given level of magnification from ") +print(" (a list of given) images for quick inspection.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import numpy as np +import glob +from pathlib import Path + +# for argument parser +from sys import exit, argv +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +parser = argparse.ArgumentParser( + prog='slideMacro', + description='This script will get macro-images at a given level of magnification from (a list of given) images for quick inspection.', + usage='slideMacro -i/--input -l/--level; optional: -d/--display -o/--outdir -s/--suffix -t/--type -f/--force -v/--verbose; for help: -h/--help', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) + +parser.add_argument('-d', '--display', help="Also show macro on display, default simply writes macro.", action="store_true") +parser.add_argument('-o', '--outdir', help="Output dir, default is present working directory.", default="./", type=str) +parser.add_argument('-s', '--suffix', help="Suffix to append to end of file, default is 'macro'.", default="", type=str) +parser.add_argument('-t', '--type', help="Output file type, default is png (which is slower), other options are tif.", default="png", type=str) +parser.add_argument('-f', '--force', help="Force utput even if it exists.", default=False, action="store_true") +parser.add_argument('-v', '--verbose', help="While writing images also display image properties.", default=False, action="store_true") + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument('-i','--input',help="Input (directory containing files). Try: *.TIF or /path_to/images/*.ndpi.", nargs="*") +requiredNamed.add_argument('-l','--level',help="Magnification level to extract, with '5' as level 5. Try: 5.") + +args = parser.parse_args() + +if not args.input: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideMacro ***!") + print("Note that -i/--input is required. Try: *.TIF or /path_to/images/*.ndpi.\n") + parser.print_help() + exit() + +if len(args.input) > 1: # bash has sent us a list of files + files = args.input +else: # user sent us a wildcard, need to use glob to find files + files = glob.glob(args.input[0]) + +# make the output directory if it does not exist +if not os.path.exists(args.outdir): + os.makedirs(args.outdir) + +for fname in files: + fnameout=f"{Path(fname).stem}{args.suffix}.!!.{args.type if args.type[0] == '' else ''+args.type}" + fnameout=Path(args.outdir,fnameout) + + if not args.force and os.path.exists(fnameout): + print(f"Skipping {fnameout} as output file exists and --force is not set") + continue + + fimage=openslide.OpenSlide(fname) + + level = int(args.level) + img = fimage.read_region((0, 0), level, fimage.level_dimensions[level]) # will get the given level of an image + img = np.asarray(img)[:,:, 0:3] + + if args.display: + print("Displaying [",fname,"] at level [",level,"].") + # Let's print each dimension of the image + print('* image dimensions (height x width in pixels):', img.shape) + ### FOR DEBUG +# print('Image data type:', img.dtype) +# print('Image type:', type(img)) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + + # To display our image variable, we use 'imshow' + # The first parameter will be title shown on image window + # The second parameter is the image variable + # rotate the image for easy reading (https://www.geeksforgeeks.org/python-opencv-cv2-rotate-method/) + img_r = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + cv2.imshow(print('Display image [',fname,'] at level [',level,']'), cv2.cvtColor(img_r, cv2.COLOR_RGB2BGR)) + print('(hit any key on the image to close)') # how waitKey works + + # waitKey - ref: https://stackoverflow.com/questions/22274789/cv2-imshow-function-is-opening-a-window-that-always-says-not-responding-pyth + # 'waitKey' allows us to wait for a key stroke + # when a image window is open + # By leaving it blank it just waits for any key to be + # pressed before continuing. + # By placing numbers (except 0), we can specify a delay for + # how long you keep the window open (time is in millisecs here) + cv2.waitKey() + # This closes all open windows + # Failure to place this will cause your program to hang + cv2.destroyAllWindows() + cv2.waitKey(1) + elif args.verbose: + print("Processing [",fname,"] at level [",level,"].") + print('* image dimensions (height x width in pixels):', img.shape) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + cv2.imwrite(str(fnameout).replace("!!","macro"),cv2.cvtColor(img,cv2.COLOR_RGB2BGR)) + else: + print("Writing macro for [",fname,"] at level [",level,"].") + cv2.imwrite(str(fnameout).replace("!!","macro"),cv2.cvtColor(img,cv2.COLOR_RGB2BGR)) + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/slideMask b/slideMask index cebddb8..57b6209 100755 --- a/slideMask +++ b/slideMask @@ -3,7 +3,8 @@ # Description: Create scaled mask and macro from a virtual slide # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -33,7 +34,7 @@ SUPPORTED_FORMATS=("TIFF" "Aperio" "Hamamatsu" "iScan" "iScanHT" "Leica") DEFAULT_MASKSUFFIX=".mask" DEFAULT_MACROSUFFIX=".macro" DEFAULT_MACRO_THUMBSIZE="2000x2000" # Maximum size of macro image -DEFAULT_QSUBARGS="-pe threaded 1 -q veryshort" # THIS NEEDS UPDATING DUE TO THE NEW CENTOS7 SYSTEM +DEFAULT_QSUBARGS="-l h_rt=00:15:00 -l h_vmem=16G" # Maximum values: rt=runtime HH:MM:SS vmem=GB DEFAULT_MASKARGS="-blur 40x40 -fuzz 4%" # Errors go to stderr @@ -51,8 +52,8 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. @@ -71,6 +72,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -115,7 +118,7 @@ supported formats: ${SUPPORTED_FORMATS[@]} You can mask parts of your image that you do not wat to process. This program will use 'convert' to create a mask for you which you can later edit if needed. The mask is a layer within the original TIF file. The -slideToolkit excludes real black in further processing (color #000000). +slideToolKit excludes real black in further processing (color #000000). Two new files will be created, one file.macro.png, and one masked macro file (file.mask.png). Definitions of masking can be set with the --mask-args flag. A blur creates space between the mask and the tissue. @@ -127,9 +130,12 @@ virtual slide format is not known. For these instances you need to run slideInfo to find the correct layer manually. When the mask layer is identified (approximately 2000x2000 pixels), use the --layer flag. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. + +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } @@ -342,13 +348,32 @@ programOutput(){ # - now everything white is converted to transparent. # - this image is now put on-top of the original one. + # 20220315: There was an update to convert, having to do with the difference + # between IM6 and IM7 causing a (weird) behaviour where the non-masked area is + # completely whitened. + # Obviously we do not want this, this cause the tiling to go all wonky. + # Reference: https://imagemagick.org/script/porting.php + # IF this script is causing problems, it's probably due to this difference. + # THEORETICALLY slideToolKit in combination with CellProfiler 2.2.0, Java 8, Xcode, + # and homebrew should work out-of-the-box without `-channel RGB -negate`. No guarantee though. + # + +# convert "$MACRO" \( "$MACRO" \ +# -bordercolor white -border 100x100 \ +# $MASKARGS -transparent white \ +# -crop +100+100 -crop -100-100 +repage \ +# -alpha extract \ +# -transparent white \) \ +# -flatten "$MASK" + convert "$MACRO" \( "$MACRO" \ -bordercolor white -border 100x100 \ $MASKARGS -transparent white \ -crop +100+100 -crop -100-100 +repage \ -alpha extract \ - -transparent white \) \ + -transparent white -channel RGB -negate \) \ -flatten "$MASK" + } # all check? checkRequirements @@ -356,4 +381,4 @@ checkDependencies # lets go! # actual program -programOutput \ No newline at end of file +programOutput diff --git a/slideMaskEntropy.py b/slideMaskEntropy.py deleted file mode 100755 index 50089d8..0000000 --- a/slideMaskEntropy.py +++ /dev/null @@ -1,93 +0,0 @@ -#!/usr/local/bin/python - -print "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" -print " slideToolkit Entropy Filter" -print " version 1.0" -print "" -print "* Written by : Tim Bezemer" -print "* E-mail : t.bezemer-2@umcutrecht.nl" -print "* Suggested for by : Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl" -print "* Last update : 2016-08-12" -print "* Version : 1.2.20160812" -print "" -print "* Description : Makes maskers of PNG files using a entropy-filter. To be" -print " used when the regular mask-making program (slideMask)" -print " is insufficient." -print "" -print "* USAGE: for DIR in $(ls) ; do echo \"* Processing [ \"$DIR\" ]...\"; $(command -v slideMaskEntropy.py) \"$DIR\"/\"$DIR\".macro.png; done" -print " slideMaskEntropy.py [FILE.png]" -print "" -print "" -print "* PREREQUISITES: The packages 'scikit-image', 'numpy', and 'matplotlib' are required." -print " Installation instructions:" -print " pip install numpy" -print " pip install matplotlib" -print " pip install scikit-image" -print "" -print "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" - -import matplotlib -matplotlib.use("Agg") -import matplotlib.pyplot as plt -import matplotlib.cm as cm - -from skimage import data -from skimage.filters.rank import entropy -from skimage.morphology import disk -from skimage.util import img_as_ubyte -from skimage.io import imread, imsave - -from sys import exit -from sys import argv -import os.path - -import numpy as np - -import warnings -warnings.filterwarnings("ignore") - - -def filter_entropy_image(image, filter): - - eimage = entropy(image, disk(5)) - - new_picture = np.ndarray(shape=eimage.shape) #[[False] * image.shape[1]] * image.shape[0] - - for rn, row in enumerate(eimage): - - for pn, pixel in enumerate(row): - - if pixel < filter: - - new_picture[rn,pn] = True - - else: - new_picture[rn,pn] = False - - return new_picture.astype('b') - -fn = argv[1] - -if not os.path.isfile(fn): - print "No such file" - sys.exit() - - -print "\tCreating tissue mask (entropy filter)..." - -image = imread(fn, True) - -entropy_image = filter_entropy_image(image, 3.5) - -plt.axis('off') - -newfile = fn - -newfile = newfile.replace(".macro.png", ".emask.png") - -#np.save(newfile, entropy_image) - -plt.imsave(newfile, entropy_image, cmap=plt.cm.binary) - -print "\t>>> Saved image as:" + newfile - diff --git a/slideQuantify b/slideQuantify new file mode 100755 index 0000000..5a8392e --- /dev/null +++ b/slideQuantify @@ -0,0 +1,316 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #3 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #4 -- starting letters/characters of the image-subdirectories, e.g. IMG or AE or AAA." + echoerror "- Argument #5 -- output filename where the CellProfiler results are stored, e.g. Image.csv (delimiter is assumed '_')." + echoerror "- Argument #6 -- email address to send aborted/error jobs too, e.g. y.name@umcutrecht.nl." + echoerror "- Argument #7 -- by default emails are send when jobs are aborted/error, you can change that here, e.g. you can choose: BEGIN=begin of job; END=end of job; FAIL=abort of job; ALL=all message job; NONE=no mail is send." + echoerror "- Argument #8 -- Content threshold. The percentage of tissue that should be present at the minimum in a given tile, e.g. 0.5 (5%)." + echoerror "- Argument #9 -- Patch size. Size of the tiles in pixels, e.g. 512 indicates a tile of 512x512; slideToolKit was tested using patches of 2000x2000." + echoerror "- Argument #10 -- Down-sampling. Down-sampling number, to indicate the magnification to use, e.g. with a maximum magnification of 40x, a 2 indcates a downsample to 20x." + echoerror "- Argument #11 -- Masking method to apply. Options are otsu, adaptive, or graph (segmentation based)." + echoerror "- Argument #12 -- Unique label. A string-number combination (no spaces) for reference to the analysis run. [OPTIONAL]" + echoerror "- Argument #13 -- Batch size. A number to indicate the batch size, e.g. '5000'. This enables running only few samples for testing or debugging purposes. [OPTIONAL]" + echoerror "- Argument #14 -- Random sample. A number to indicate the number of overlay-images after analysis to keep, e.g. '20', by default set at '50'. [OPTIONAL]" + echoerror "" + echoerror "An example command would be: slideQuantify [arg1: STAIN] [arg2: path_to_cellprofiler_pipeline] [arg3: path_to_working_directory] [arg4: IMG or AE or AAA] [arg5: Image.csv] [arg6: y.name@umcutrecht.nl] [arg7: FAIL] [arg8: 5] [arg9: 2000] [arg10: 2] [arg11: adaptive] " + echoerror "" + echoerror "Please note that this command should be run within the working directory (Argument #3)." + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-25" +echoitalic "* Version : 2.1.0" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory. " +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="${1}" # Depends on arg1 +PIPELINE="${2}" # Depends on arg2 +PROJECTDIR="${3}" # Depends on arg3 +FILESTART="${4}" # Depends on arg4 +OUTPUTFILENAME="${5}" # Depends on arg5 +EMAIL="${6}" # Depends on arg6 +MAILSETTING="${7}" # Depends on arg7 + +# EMASKTHRESHOLD="$8" # Depends on arg8 + +CONTENTTHRESHOLD="${8}" # Depends on arg8 +PATCHSIZE="${9}" # Depends on arg9 +OUTPUTDOWN="${10}" # Depends on arg10 +MASKMETHOD="${11}" # Depends on arg11 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +UNIQUE_LABEL=${12-V1} # Depends on arg12 +BATCH_SIZE=${13-5000} # Depends on arg13 +RANDOM_SAMPLE=${14-50} # Depends on arg14 + +# QJOB_MEM_SLIDEMASK="100G" +# QJOB_TIME_SLIDEMASK="02:30:00" +# +# QJOB_MEM_SLIDE2TILES="180G" +# QJOB_TIME_SLIDE2TILES="04:30:00" +# +# QJOB_MEM_SLIDENORM="12G" +# QJOB_TIME_SLIDENORM="02:00:00" +# +# QJOB_MEM_CP="12G" +# QJOB_TIME_CP="12:00:00" + +QJOB_MEM_SLIDEMASK="8G" +QJOB_TIME_SLIDEMASK="00:15:00" + +QJOB_MEM_SLIDE2TILES="8G" +QJOB_TIME_SLIDE2TILES="00:30:00" + +QJOB_MEM_SLIDENORM="8G" +QJOB_TIME_SLIDENORM="00:15:00" + +QJOB_MEM_CP="12G" +QJOB_TIME_CP="13:00:00" + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +# Set ExpressHIST DIRECTORY +EXPRESSHIST="/hpc/local/CentOS7/dhl_ec/software/ExpressHIST" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 12 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify ***!" + +else + echobold "Cleaning up house, first." + mkdir -pv "$PROJECTDIR"/logs + mkdir -pv "$PROJECTDIR"/errors + rm -rfv "$FILESTART"*/magick-tmp + + ENTROPY_THRESHOLD="$EMASKTHRESHOLD" + HOLD_JIDS=() + INDEX=0 + + cd $PROJECTDIR + pwd + ### FOR DEBUG + ### echocyan "Debug mode - processing debug-images." + ### DEBUGLIST="AE1088.UMC.CD3_CD56_NKT" + ### DEBUGLIST="AE1088.UMC.CD3_CD56_NKT AE2292.UMC.CD3_CD56_NKT AE861.UMC.CD3_CD56_NKT AE3892.UMC.CD3_CD56_NKT" + ### for SLIDE_NUM in $DEBUGLIST; do + ### AE1088.UMC.CD3_CD56_NKT AE1165.UMC.CD3_CD56_NKT AE1249.UMC.CD3_CD56_NKT AE1462.UMC.CD3_CD56_NKT AE1468.UMC.CD3_CD56_NKT AE1495.UMC.CD3_CD56_NKT AE1548.UMC.CD3_CD56_NKT AE1550.UMC.CD3_CD56_NKT AE1701.UMC.CD3_CD56_NKT AE1707.CD3_CD56_NKT.v2 AE1710.UMC.CD3_CD56_NKT AE1713.UMC.CD3_CD56_NKT AE1722.UMC.CD3_CD56_NKT AE1731.UMC.CD3_CD56_NKT AE1732.UMC.CD3_CD56_NKT AE1737.UMC.CD3_CD56_NKT AE1750.UMC.CD3_CD56_NKT AE1771.UMC.CD3_CD56_NKT AE1780.UMC.CD3_CD56_NKT AE1781.UMC.CD3_CD56_NKT AE1857.UMC.CD3_CD56_NKT.2 AE1858.UMC.CD3_CD56_NKT AE1872.UMC.CD3_CD56_NKT AE1878.UMC.CD3_CD56_NKT AE1897.UMC.CD3_CD56_NKT AE1969.UMC.CD3_CD56_NKT AE2107.UMC.CD3_CD56_NKT AE2131.UMC.CD3_CD56_NKT AE2138.CD45CD3.v2goed AE2139.UMC.CD3_CD56_NKT.2 AE2157.CD3_CD56_NKT.v2 AE2160.UMC.CD3_CD56_NKT AE2163.UMC.CD3_CD56_NKT.2 AE2170.UMC.CD3_CD56_NKT AE2176.UMC.CD3_CD56_NKT AE2183.UMC.CD3_CD56_NKT AE2188.UMC.CD3_CD56_NKT AE2197.CD3_CD56_NKT.v2 AE21A.UMC.CD3_CD56_NKT.2 AE21B.UMC.CD3_CD56_NKT.2 AE2200.CD3_CD56_NKT.v2 AE2211.UMC.CD3_CD56_NKT AE2227.UMC.CD3_CD56_NKT AE2254.UMC.CD3_CD56_NKT AE2255.UMC.CD3_CD56_NKT AE2259.UMC.CD3_CD56_NKT AE2270.UMC.CD3_CD56_NKT.2 AE2281.UMC.CD3_CD56_NKT AE2292.UMC.CD3_CD56_NKT AE2293.UMC.CD3_CD56_NKT AE2295.UMC.CD3_CD56_NKT AE2296.CD3_CD56_NKT.v2 AE2296.UMC.CD3_CD56_NKT.2 AE2364.UMC.CD3_CD56_NKT AE2376.UMC.CD3_CD56_NKT AE2385.UMC.CD3_CD56_NKT AE2427.UMC.CD3_CD56_NKT.2 AE2433.UMC.CD3_CD56_NKT.2 AE2539.UMC.CD3_CD56_NKT AE2601.CD3_CD56_NKT.v2 AE2601.UMC.CD3_CD56_NKT.2 AE2613.UMC.CD3_CD56_NKT AE2637.UMC.CD3_CD56_NKT AE2649.UMC.CD3_CD56_NKT.2 AE2658.UMC.CD3_CD56_NKT AE2659.UMC.CD3_CD56_NKT AE2682.UMC.CD3_CD56_NKT AE2684.UMC.CD3_CD56_NKT AE2753.UMC.CD3_CD56_NKT AE2754.CD3_CD56_NKT.v2 AE2754.UMC.CD3_CD56_NKT.2 AE2757.UMC.CD3_CD56_NKT AE2759.UMC.CD3_CD56_NKT AE276.UMC.CD3_CD56_NKT AE285.UMC.CD3_CD56_NKT AE2953.CD3_CD56_NKT.v2 AE2953.UMC.CD3_CD56_NKT.2 AE2963.CD3_CD56_NKT.v2 AE2963.UMC.CD3_CD56_NKT.2 AE296.UMC.CD3_CD56_NKT AE2973.UMC.CD3_CD56_NKT.2 AE2990.UMC.CD3_CD56_NKT.2 AE3023.CD3_CD56_NKT.v2 AE3023.UMC.CD3_CD56_NKT.2 AE3136.UMC.CD3_CD56_NKT.2 AE3148.CD3_CD56_NKT.v2 AE3148.UMC.CD3_CD56_NKT.2 AE3251.CD3_CD56_NKT.v2 AE3251.UMC.CD3_CD56_NKT.2 AE3255.CD3_CD56_NKT.v2 AE3255.UMC.CD3_CD56_NKT.2 AE3305.UMC.CD3_CD56_NKT AE3348.UMC.CD3_CD56_NKT.2 AE3350.UMC.CD3_CD56_NKT.2 AE3680.UMC.CD3_CD56_NKT AE3690.UMC.CD3_CD56_NKT.2 AE3691.UMC.CD3_CD56_NKT.2 AE3762.UMC.CD3_CD56_NKT.2 AE3763.UMC.CD3_CD56_NKT.2 AE3764.UMC.CD3_CD56_NKT.2 AE3769.UMC.CD3_CD56_NKT.2 AE3777.UMC.CD3_CD56_NKT AE3795.UMC.CD3_CD56_NKT AE3796.UMC.CD3_CD56_NKT AE3886.UMC.CD3_CD56_NKT AE3892.UMC.CD3_CD56_NKT AE410.UMC.CD3_CD56_NKT AE424.UMC.CD3_CD56_NKT AE460.UMC.CD3_CD56_NKT AE488.UMC.CD3_CD56_NKT AE737.UMC.CD3_CD56_NKT AE815.UMC.CD3_CD56_NKT AE826.UMC.CD3_CD56_NKT AE838.UMC.CD3_CD56_NKT AE861.UMC.CD3_CD56_NKT + ### for SLIDE_NUM in AE3795.UMC.CD3_CD56_NKT; do + + for SLIDE_NUM in "$FILESTART"*; do + sleep 1 + THIS_SLIDE=${SLIDE_NUM/\//} + THIS_SLIDEMASK=${THIS_SLIDE}_slideMask_${UNIQUE_LABEL} + THIS_SLIDE2TILES=${THIS_SLIDE}_slide2Tiles_${UNIQUE_LABEL} + THIS_SLIDENORMALIZE=${THIS_SLIDE}_slideNormalize_${UNIQUE_LABEL} + THIS_CELLPROFILER=${THIS_SLIDE}_CellProfiler_${UNIQUE_LABEL} + THIS_WRAPUP=${THIS_SLIDE}_WrapUp_${UNIQUE_LABEL} + + HOLD_JID_INDEX=$(expr ${INDEX} % ${BATCH_SIZE}) + + # process batches in smaller chunks + if [ "$INDEX" -lt "$BATCH_SIZE" ]; then + HOLD_JID_STRING="" + else + HOLD_JID_STRING="-hold_jid ${HOLD_JIDS[$HOLD_JID_INDEX]}" + fi + HOLD_JIDS[$HOLD_JID_INDEX]=$THIS_CELLPROFILER + + echobold "Processing images of file [ ${SLIDE_NUM} ] in directory [ ${PROJECTDIR} ] for staining [ ${STAIN} ] -- at index [ ${INDEX} ] with total batch size [ ${BATCH_SIZE} ]." + cd "$SLIDE_NUM" || exit + echo "" + pwd + + # Checking if an analysis was done before + if [[ -s cp_output/"$STAIN"_Image.csv.gz ]]; then + echoitalic "Checking if this file was processed before; cp_output/${STAIN}_Image.csv.gz should be present ....." + ls -lh cp_output/*.gz + cd .. ; + INDEX=$((INDEX+1)); + echo "..... Analysis was already done!"; + continue; + fi + + echo "..... Processing file for the first time. Submitting job for: " + + rm -rfv "$PROJECTDIR"/logs/"$SLIDE_NUM"* + rm -rfv "$PROJECTDIR"/errors/"$SLIDE_NUM"* + + + ### slideMasking and slideTiling + echo "" + echoitalic "* slideMask to mask images using adaptive, otsu or graph segmentation based thresholding. [ $THIS_SLIDEMASK ]" + + JOB_ID_THIS_SLIDEMASK=$(sbatch -J ${THIS_SLIDEMASK} -e ${PROJECTDIR}/errors/slidemask.${THIS_SLIDEMASK}.errors -o ${PROJECTDIR}/logs/slidemask.${THIS_SLIDEMASK}.log ${HOLD_JID_STRING} --mem=${QJOB_MEM_SLIDEMASK} --time=${QJOB_TIME_SLIDEMASK} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable ${SLIDETOOLKITDIR}/slideQuantify_1_expresshist_mask.sh ${EXPRESSHIST} ${CONTENTTHRESHOLD} ${PATCHSIZE} ${OUTPUTDOWN} ${MASKMETHOD} ) + + ### FOR DEBUG + echocyan "Debug mode - listing slide number and job-id." + echo $THIS_SLIDEMASK + echo $HOLD_JID_STRING + + ### slide2Tiles + echo "" + echoitalic "* slide2Tiles to create tiles from images. [ $THIS_SLIDE2TILES ]" + + JOB_ID_THIS_SLIDE2TILES=$(sbatch -J ${THIS_SLIDE2TILES} -e ${PROJECTDIR}/errors/slide2tiles.${THIS_SLIDE2TILES}.errors -o ${PROJECTDIR}/logs/slide2tiles.${THIS_SLIDE2TILES}.log --mem=${QJOB_MEM_SLIDE2TILES} --time=${QJOB_TIME_SLIDE2TILES} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --depend=afterany:$JOB_ID_THIS_SLIDEMASK --parsable ${SLIDETOOLKITDIR}/slideQuantify_2_expresshist_tile.sh ${EXPRESSHIST} ${CONTENTTHRESHOLD} ${PATCHSIZE} ${OUTPUTDOWN} ${MASKMETHOD} ) + ### FOR DEBUG + ### JOB_ID_THIS_SLIDE2TILES=$(sbatch -J ${THIS_SLIDE2TILES} -e ${PROJECTDIR}/errors/slide2tiles.${THIS_SLIDE2TILES}.errors -o ${PROJECTDIR}/logs/slide2tiles.${THIS_SLIDE2TILES}.log --mem=${QJOB_MEM_SLIDE2TILES} --time=${QJOB_TIME_SLIDE2TILES} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable ${SLIDETOOLKITDIR}/slideQuantify_2_expresshist_tile.sh ${EXPRESSHIST} ${CONTENTTHRESHOLD} ${PATCHSIZE} ${OUTPUTDOWN} ${MASKMETHOD} ) + + ### slideNormalize + echo "" + echoitalic "* slideNormalize for normalization of images. [ $THIS_SLIDENORMALIZE ]" + + JOB_ID_THIS_SLIDENORMALIZE=$(sbatch -J ${THIS_SLIDENORMALIZE} -e ${PROJECTDIR}/errors/slidenormalize.${THIS_SLIDENORMALIZE}.errors -o ${PROJECTDIR}/logs/slidenormalize.${THIS_SLIDENORMALIZE}.log --mem=${QJOB_MEM_SLIDENORM} --time=${QJOB_TIME_SLIDENORM} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --depend=afterany:$JOB_ID_THIS_SLIDE2TILES --parsable ${SLIDETOOLKITDIR}/slideQuantify_3_tile_normalizing.sh ) + ### FOR DEBUG + ### JOB_ID_THIS_SLIDENORMALIZE=$(sbatch -J ${THIS_SLIDENORMALIZE} -e ${PROJECTDIR}/errors/slidenormalize.${THIS_SLIDENORMALIZE}.errors -o ${PROJECTDIR}/logs/slidenormalize.${THIS_SLIDENORMALIZE}.log --mem=${QJOB_MEM_SLIDENORM} --time=${QJOB_TIME_SLIDENORM} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable ${SLIDETOOLKITDIR}/slideQuantify_3_tile_normalizing.sh ) + + ### CellProfiler + echo "" + echoitalic "* CellProfiler to analyze images. [ $THIS_CELLPROFILER ]" + + JOB_ID_THIS_CELLPROFILER=$(sbatch -J ${THIS_CELLPROFILER} -e ${PROJECTDIR}/errors/slidecellprofiler.${THIS_CELLPROFILER}.errors -o ${PROJECTDIR}/logs/slidecellprofiler.${THIS_CELLPROFILER}.log --mem=${QJOB_MEM_CP} --time=${QJOB_TIME_CP} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --depend=afterany:$JOB_ID_THIS_SLIDENORMALIZE --parsable ${SLIDETOOLKITDIR}/slideQuantify_cellprofiler.sh ${PIPELINE} ${STAIN} ${SLIDE_NUM} ) + ### FOR DEBUG + ### JOB_ID_THIS_CELLPROFILER=$(sbatch -J ${THIS_CELLPROFILER} -e ${PROJECTDIR}/errors/slidecellprofiler.${THIS_CELLPROFILER}.errors -o ${PROJECTDIR}/logs/slidecellprofiler.${THIS_CELLPROFILER}.log --mem=${QJOB_MEM_CP} --time=${QJOB_TIME_CP} --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable ${SLIDETOOLKITDIR}/slideQuantify_cellprofiler.sh ${PIPELINE} ${STAIN} ${SLIDE_NUM} ) + + + ### Wrap Up + echo "" + echoitalic "* WrapUp the work we have done, and clean house. [ $THIS_WRAPUP ]" + + JOB_ID_THIS_WRAPUP=$(sbatch -J ${THIS_WRAPUP} -e ${PROJECTDIR}/errors/slidewrapup.${THIS_WRAPUP}.errors -o ${PROJECTDIR}/logs/slidewrapup.${THIS_WRAPUP}.log --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable --depend=afterany:$JOB_ID_THIS_CELLPROFILER ${SLIDETOOLKITDIR}/slideQuantify_wrapup.sh ${STAIN} ${OUTPUTFILENAME} ${RANDOM_SAMPLE} ) + ### FOR DEBUG + ### JOB_ID_THIS_WRAPUP=$(sbatch -J ${THIS_WRAPUP} -e ${PROJECTDIR}/errors/slidewrapup.${THIS_WRAPUP}.errors -o ${PROJECTDIR}/logs/slidewrapup.${THIS_WRAPUP}.log --mail-type=${MAILSETTING} --mail-user=${EMAIL} --parsable ${SLIDETOOLKITDIR}/slideQuantify_wrapup.sh ${STAIN} ${OUTPUTFILENAME} ${RANDOM_SAMPLE} ) + + echo "" + echo "* Moving back to the project directory [$PROJECTDIR]" + cd $PROJECTDIR + + ### REMOVE THIS BREAK FOR PRODUCTION! + ### When this break is active, only the first item + ### in the list of items to be processed will be processed. + ### + ### OLD CODE + ### INDEX=$((INDEX+1)) + ### break; + + ### NEW CODE + INDEX=$((INDEX+1)) + if [ "$INDEX" -eq "$BATCH_SIZE" ]; then + break; + fi + + # END OF for-loop + done + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + + diff --git a/slideQuantifyOSX b/slideQuantifyOSX new file mode 100755 index 0000000..f5aaa2f --- /dev/null +++ b/slideQuantifyOSX @@ -0,0 +1,487 @@ +#!/bin/bash +# +# Description: slideQuantify for local (macOS) use to debug and test workflows and +# CellProfiler pipelines, or simply to analyze (small) batches of +# slides locally. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #3 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #4 -- starting letters/characters of the image-subdirectories, e.g. IMG or AE or AAA." + echoerror "- Argument #5 -- output filename where the CellProfiler results are stored, e.g. Image.csv (delimiter is assumed '_')." + echoerror "- Argument #6 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "- Argument #7 -- Unique label. A string-number combination (no spaces) for reference to the analysis run. [OPTIONAL]" + echoerror "- Argument #8 -- Batch size. A number to indicate the batch size, e.g. '5000'. [OPTIONAL]" + echoerror "- Argument #9 -- Random sample. A number to indicate the number of overlay-images after analysis to keep, e.g. '20'. [OPTIONAL]" + echoerror "" + echoerror "An example command would be: slideQuantifyOSX [arg1: STAIN] [arg2: path_to_cellprofiler_pipeline] [arg3: path_to_working_directory] [arg4: IMG or AE or AAA] [arg5: Image.txt] [arg6: 210]" + echoerror "" + echoerror "Please note that this command should be run within the working directory (Argument #3)." + + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantifyOSX" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-10" +echoitalic "* Version : 2.0.6" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory on macOS, for instance" +echoitalic " your iMac, mac mini or MacBook" +echo "" +echocyan "NOTE: this *only* works on macOS (10.14.5+) with Anaconda 3.8 and brew." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +PIPELINE="$2" # Depends on arg2 +PROJECTDIR="$3" # Depends on arg3 +FILESTART="$4" # Depends on arg4 +OUTPUTFILENAME="$5" # Depends on arg5 +EMASKTHRESHOLD="$6" # Depends on arg6 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +UNIQUE_LABEL=${7-V1} # Depends on arg7 +BATCH_SIZE=${8-5000} # Depends on arg8 +RANDOM_SAMPLE=${9-50} # Depends on arg9 + +### Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="$HOME/git/swvanderlaan/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 6 ]]; then + echo "Oh, computer says no! Number of arguments found "$#"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantifyOSX ***!" + +else + echobold "Cleaning up house, first." + rm -rfv "$FILESTART*"/magick-tmp + + echobold "Starting up the virtual environment." + + source activate cp4 + + ### FOR DEBUG + ### echobold "Testing the virtual environment." + ### + ### python $SLIDETOOLKITDIR/slideToolKitTest.py + + echobold "Alright. All is set." + ENTROPY_THRESHOLD="$EMASKTHRESHOLD" +# HOLD_JIDS=() + INDEX=0 + #BATCH_SIZE=5000 # should be argument, but how exactly does this work? + #UNIQUE_LABEL="V3" # should be argument + + cd $PROJECTDIR + + ### FOR DEBUG + ### echocyan "Debug mode - processing debug-images." + ### DEBUGLIST="AE1088.UMC.CD3_CD56_NKT" + ### DEBUGLIST="AE1088.UMC.CD3_CD56_NKT AE2292.UMC.CD3_CD56_NKT AE861.UMC.CD3_CD56_NKT AE3892.UMC.CD3_CD56_NKT" + ### for SLIDE_NUM in $DEBUGLIST; do + ### for SLIDE_NUM in AE1088.UMC.CD3_CD56_NKT; do + ### AE1088.UMC.CD3_CD56_NKT AE1165.UMC.CD3_CD56_NKT AE1249.UMC.CD3_CD56_NKT AE1462.UMC.CD3_CD56_NKT AE1468.UMC.CD3_CD56_NKT AE1495.UMC.CD3_CD56_NKT AE1548.UMC.CD3_CD56_NKT AE1550.UMC.CD3_CD56_NKT AE1701.UMC.CD3_CD56_NKT AE1707.CD3_CD56_NKT.v2 AE1710.UMC.CD3_CD56_NKT AE1713.UMC.CD3_CD56_NKT AE1722.UMC.CD3_CD56_NKT AE1731.UMC.CD3_CD56_NKT AE1732.UMC.CD3_CD56_NKT AE1737.UMC.CD3_CD56_NKT AE1750.UMC.CD3_CD56_NKT AE1771.UMC.CD3_CD56_NKT AE1780.UMC.CD3_CD56_NKT AE1781.UMC.CD3_CD56_NKT AE1857.UMC.CD3_CD56_NKT.2 AE1858.UMC.CD3_CD56_NKT AE1872.UMC.CD3_CD56_NKT AE1878.UMC.CD3_CD56_NKT AE1897.UMC.CD3_CD56_NKT AE1969.UMC.CD3_CD56_NKT AE2107.UMC.CD3_CD56_NKT AE2131.UMC.CD3_CD56_NKT AE2138.CD45CD3.v2goed AE2139.UMC.CD3_CD56_NKT.2 AE2157.CD3_CD56_NKT.v2 AE2160.UMC.CD3_CD56_NKT AE2163.UMC.CD3_CD56_NKT.2 AE2170.UMC.CD3_CD56_NKT AE2176.UMC.CD3_CD56_NKT AE2183.UMC.CD3_CD56_NKT AE2188.UMC.CD3_CD56_NKT AE2197.CD3_CD56_NKT.v2 AE21A.UMC.CD3_CD56_NKT.2 AE21B.UMC.CD3_CD56_NKT.2 AE2200.CD3_CD56_NKT.v2 AE2211.UMC.CD3_CD56_NKT AE2227.UMC.CD3_CD56_NKT AE2254.UMC.CD3_CD56_NKT AE2255.UMC.CD3_CD56_NKT AE2259.UMC.CD3_CD56_NKT AE2270.UMC.CD3_CD56_NKT.2 AE2281.UMC.CD3_CD56_NKT AE2292.UMC.CD3_CD56_NKT AE2293.UMC.CD3_CD56_NKT AE2295.UMC.CD3_CD56_NKT AE2296.CD3_CD56_NKT.v2 AE2296.UMC.CD3_CD56_NKT.2 AE2364.UMC.CD3_CD56_NKT AE2376.UMC.CD3_CD56_NKT AE2385.UMC.CD3_CD56_NKT AE2427.UMC.CD3_CD56_NKT.2 AE2433.UMC.CD3_CD56_NKT.2 AE2539.UMC.CD3_CD56_NKT AE2601.CD3_CD56_NKT.v2 AE2601.UMC.CD3_CD56_NKT.2 AE2613.UMC.CD3_CD56_NKT AE2637.UMC.CD3_CD56_NKT AE2649.UMC.CD3_CD56_NKT.2 AE2658.UMC.CD3_CD56_NKT AE2659.UMC.CD3_CD56_NKT AE2682.UMC.CD3_CD56_NKT AE2684.UMC.CD3_CD56_NKT AE2753.UMC.CD3_CD56_NKT AE2754.CD3_CD56_NKT.v2 AE2754.UMC.CD3_CD56_NKT.2 AE2757.UMC.CD3_CD56_NKT AE2759.UMC.CD3_CD56_NKT AE276.UMC.CD3_CD56_NKT AE285.UMC.CD3_CD56_NKT AE2953.CD3_CD56_NKT.v2 AE2953.UMC.CD3_CD56_NKT.2 AE2963.CD3_CD56_NKT.v2 AE2963.UMC.CD3_CD56_NKT.2 AE296.UMC.CD3_CD56_NKT AE2973.UMC.CD3_CD56_NKT.2 AE2990.UMC.CD3_CD56_NKT.2 AE3023.CD3_CD56_NKT.v2 AE3023.UMC.CD3_CD56_NKT.2 AE3136.UMC.CD3_CD56_NKT.2 AE3148.CD3_CD56_NKT.v2 AE3148.UMC.CD3_CD56_NKT.2 AE3251.CD3_CD56_NKT.v2 AE3251.UMC.CD3_CD56_NKT.2 AE3255.CD3_CD56_NKT.v2 AE3255.UMC.CD3_CD56_NKT.2 AE3305.UMC.CD3_CD56_NKT AE3348.UMC.CD3_CD56_NKT.2 AE3350.UMC.CD3_CD56_NKT.2 AE3680.UMC.CD3_CD56_NKT AE3690.UMC.CD3_CD56_NKT.2 AE3691.UMC.CD3_CD56_NKT.2 AE3762.UMC.CD3_CD56_NKT.2 AE3763.UMC.CD3_CD56_NKT.2 AE3764.UMC.CD3_CD56_NKT.2 AE3769.UMC.CD3_CD56_NKT.2 AE3777.UMC.CD3_CD56_NKT AE3795.UMC.CD3_CD56_NKT AE3796.UMC.CD3_CD56_NKT AE3886.UMC.CD3_CD56_NKT AE3892.UMC.CD3_CD56_NKT AE410.UMC.CD3_CD56_NKT AE424.UMC.CD3_CD56_NKT AE460.UMC.CD3_CD56_NKT AE488.UMC.CD3_CD56_NKT AE737.UMC.CD3_CD56_NKT AE815.UMC.CD3_CD56_NKT AE826.UMC.CD3_CD56_NKT AE838.UMC.CD3_CD56_NKT AE861.UMC.CD3_CD56_NKT + ### for SLIDE_NUM in AE1088.UMC.CD3 AE1165.UMC.CD3_CD56_NKT AE1249.UMC.CD3_CD56_NKT AE1462.UMC.CD3_CD56_NKT AE1468.UMC.CD3_CD56_NKT AE1495.UMC.CD3_CD56_NKT AE1548.UMC.CD3_CD56_NKT AE1550.UMC.CD3_CD56_NKT AE1701.UMC.CD3_CD56_NKT AE1707.CD3_CD56_NKT.v2 AE1710.UMC.CD3_CD56_NKT AE1713.UMC.CD3_CD56_NKT AE1722.UMC.CD3_CD56_NKT AE1731.UMC.CD3_CD56_NKT AE1732.UMC.CD3_CD56_NKT AE1737.UMC.CD3_CD56_NKT AE1750.UMC.CD3_CD56_NKT AE1771.UMC.CD3_CD56_NKT AE1780.UMC.CD3_CD56_NKT AE1781.UMC.CD3_CD56_NKT AE1857.UMC.CD3_CD56_NKT.2 AE1858.UMC.CD3_CD56_NKT AE1872.UMC.CD3_CD56_NKT AE1878.UMC.CD3_CD56_NKT AE1897.UMC.CD3_CD56_NKT AE1969.UMC.CD3_CD56_NKT AE2107.UMC.CD3_CD56_NKT AE2131.UMC.CD3_CD56_NKT AE2138.CD45CD3.v2goed AE2139.UMC.CD3_CD56_NKT.2 AE2157.CD3_CD56_NKT.v2 AE2160.UMC.CD3_CD56_NKT AE2163.UMC.CD3_CD56_NKT.2 AE2170.UMC.CD3_CD56_NKT AE2176.UMC.CD3_CD56_NKT AE2183.UMC.CD3_CD56_NKT AE2188.UMC.CD3_CD56_NKT AE2197.CD3_CD56_NKT.v2 AE21A.UMC.CD3_CD56_NKT.2 AE21B.UMC.CD3_CD56_NKT.2 AE2200.CD3_CD56_NKT.v2 AE2211.UMC.CD3_CD56_NKT AE2227.UMC.CD3_CD56_NKT AE2254.UMC.CD3_CD56_NKT AE2255.UMC.CD3_CD56_NKT AE2259.UMC.CD3_CD56_NKT AE2270.UMC.CD3_CD56_NKT.2 AE2281.UMC.CD3_CD56_NKT AE2292.UMC.CD3_CD56_NKT AE2293.UMC.CD3_CD56_NKT AE2295.UMC.CD3_CD56_NKT AE2296.CD3_CD56_NKT.v2 AE2296.UMC.CD3_CD56_NKT.2 AE2364.UMC.CD3_CD56_NKT AE2376.UMC.CD3_CD56_NKT AE2385.UMC.CD3_CD56_NKT AE2427.UMC.CD3_CD56_NKT.2 AE2433.UMC.CD3_CD56_NKT.2 AE2539.UMC.CD3_CD56_NKT AE2601.CD3_CD56_NKT.v2 AE2601.UMC.CD3_CD56_NKT.2 AE2613.UMC.CD3_CD56_NKT AE2637.UMC.CD3_CD56_NKT AE2649.UMC.CD3_CD56_NKT.2 AE2658.UMC.CD3_CD56_NKT AE2659.UMC.CD3_CD56_NKT AE2682.UMC.CD3_CD56_NKT AE2684.UMC.CD3_CD56_NKT AE2753.UMC.CD3_CD56_NKT AE2754.CD3_CD56_NKT.v2 AE2754.UMC.CD3_CD56_NKT.2 AE2757.UMC.CD3_CD56_NKT AE2759.UMC.CD3_CD56_NKT AE276.UMC.CD3_CD56_NKT AE285.UMC.CD3_CD56_NKT AE2953.CD3_CD56_NKT.v2 AE2953.UMC.CD3_CD56_NKT.2 AE2963.CD3_CD56_NKT.v2 AE2963.UMC.CD3_CD56_NKT.2 AE296.UMC.CD3_CD56_NKT AE2973.UMC.CD3_CD56_NKT.2 AE2990.UMC.CD3_CD56_NKT.2 AE3023.CD3_CD56_NKT.v2 AE3023.UMC.CD3_CD56_NKT.2 AE3136.UMC.CD3_CD56_NKT.2 AE3148.CD3_CD56_NKT.v2 AE3148.UMC.CD3_CD56_NKT.2 AE3251.CD3_CD56_NKT.v2 AE3251.UMC.CD3_CD56_NKT.2 AE3255.CD3_CD56_NKT.v2 AE3255.UMC.CD3_CD56_NKT.2 AE3305.UMC.CD3_CD56_NKT AE3348.UMC.CD3_CD56_NKT.2 AE3350.UMC.CD3_CD56_NKT.2 AE3680.UMC.CD3_CD56_NKT AE3690.UMC.CD3_CD56_NKT.2 AE3691.UMC.CD3_CD56_NKT.2 AE3762.UMC.CD3_CD56_NKT.2 AE3763.UMC.CD3_CD56_NKT.2 AE3764.UMC.CD3_CD56_NKT.2 AE3769.UMC.CD3_CD56_NKT.2 AE3777.UMC.CD3_CD56_NKT AE3795.UMC.CD3_CD56_NKT AE3796.UMC.CD3_CD56_NKT AE3886.UMC.CD3_CD56_NKT AE3892.UMC.CD3_CD56_NKT AE410.UMC.CD3_CD56_NKT AE424.UMC.CD3_CD56_NKT AE460.UMC.CD3_CD56_NKT AE488.UMC.CD3_CD56_NKT AE737.UMC.CD3_CD56_NKT AE815.UMC.CD3_CD56_NKT AE826.UMC.CD3_CD56_NKT AE838.UMC.CD3_CD56_NKT AE861.UMC.CD3_CD56_NKT; do + + for SLIDE_NUM in $FILESTART*; do + + sleep 1 + THIS_SLIDE=${SLIDE_NUM/\//} + THIS_SLIDEMASK=${THIS_SLIDE}_slideMask_${UNIQUE_LABEL} + THIS_SLIDE2TILES=${THIS_SLIDE}_slide2Tiles_${UNIQUE_LABEL} + THIS_SLIDENORMALIZE=${THIS_SLIDE}_slideNormalize_${UNIQUE_LABEL} + THIS_CELLPROFILER=${THIS_SLIDE}_CellProfiler_${UNIQUE_LABEL} + THIS_WRAPUP=${THIS_SLIDE}_WrapUp_${UNIQUE_LABEL} + + echobold "Processing images of file [ ${SLIDE_NUM} ] in directory [ ${PROJECTDIR} ] for staining [ ${STAIN} ] -- at index [ ${INDEX} ] with total batch size [ ${BATCH_SIZE} ]." + + cd "$SLIDE_NUM" || exit + echo "" + + # Checking if an analysis was done before + if [[ -s cp_output/"$STAIN"_Image.csv.gz ]]; then + echoitalic "Checking if this file was processed before; cp_output/${STAIN}_Image.csv.gz should be present ....." + ls -lh cp_output/*.gz + cd .. ; + INDEX=$((INDEX+1)); + echo "..... Analysis was already done!"; + continue; + fi + + echo "..... Processing file for the first time." + echo "..... Removing previous logs." + rm -rfv "$PROJECTDIR"/logs/"$SLIDE_NUM"* + rm -rfv "$PROJECTDIR"/errors/"$SLIDE_NUM"* + + ### slideEMask for macro, and NDPISplit when necessary + echo "" + echoitalic "* slideEMask, which will mask, create a macro, and do NDPISplit when necessary." + + # checking if masks exist - if so, skip this script + if [ -f *.emask.png ] || [ -f *.mask.png ]; then + echo "..... Masked images already exists - moving on." + + else + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + # Note that slideMask automatically determines what the layer of the macro is + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and will be converted to .tif before masking." + if [ -f *.ndpi ]; then + ndpisplit -x20 -z0 *.ndpi; + fi + + # level should be an argument (easiest), or automatically determined (harder) + # slideMacro.py -i *.ndpi -l 5 -o $(pwd) + slideMask --layer 0 -f *x20*.tif; + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif." + # level should be an argument (easiest), or automatically determined (harder) + # slideMacro.py -i *.tif -l 0 -o $(pwd) + slideMask --layer 0 -f *x20*.tif; + + elif [ -f *.TIF ]; then + echo "The image-file is a .TIF." + # level should be an argument (easiest), or automatically determined (harder) + # slideMacro.py -i *.TIF -l 3 -o $(pwd) + slideMask -f *.TIF; + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF']" + exit 1 + fi + + # running slideEMask on the macro + for MACRO in $(ls *.macro.png); do + echo "Running slideEMask on the macro-images." + slideEMask -f $MACRO -t $EMASKTHRESHOLD; + + done + + # removing temporary files + echo "..... Removing temporary directory." + rm -rfv magick-tmp + + fi + + + ### slide2Tiles + echo "" + echoitalic "* slide2Tiles to create tiles from images. [ $THIS_SLIDE2TILES ]" + + ### NOTE: this does not work locally on macOS + if [ -d *.tiles ]; then + echo "..... Tiles directory already exists - moving on." + + else + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and should first be converted to .tif before tiling." + # getting the x20 magnification + ndpisplit -x20 -z0 *.ndpi + slide2Tiles --layer 0 -f *x20*.tif -m *.emask.png --verbose; + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif." + slide2Tiles --layer 0 -f *x20*.tif -m *.emask.png --verbose; + + elif [ -f *.TIF ]; then + echo "The image-file is a .TIF." + ### old layer selection - it should fit the above mask-creation layer-selection + ### slide2Tiles --layer 8 -f *.TIF -m *.emask.png --verbose; + slide2Tiles -f *.TIF -m *.emask.png --verbose; + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF']" + exit 1 + fi + + echo "..... Removing temporary directory." + rm -rfv magick-tmp + fi + + + ### slideNormalize + echo "" + echoitalic "* slideNormalize for normalization of images. [ $THIS_SLIDENORMALIZE ]." + + ### NOTE: this does not work locally on macOS + if [ -f files2cp.txt ]; then + echo "..... Normalization was already applied - moving on." + + else + echo "..... Checking existence of tiles." + ### DEBUG + ### ls $PROJECTDIR/$SLIDE_NUM/*.tiles/; + + if [ ! -d *.tiles ]; then + (>&2 echo "*** ERROR *** No tiles to process. Create tiles first using slide2Tiles.") + exit; + + else + echo "..... Tiles present, starting normalization." + # moving to appropriate directory + cd *.tiles + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + for IMAGE_TILE in *.png; do + echo "...Processing tile [ $IMAGE_TILE ]" + echo "... - applying normalization ..." + slideNormalize $IMAGE_TILE + + echo "... - masking the normalized image ..." + slideEMask -c -f $IMAGE_TILE -t $ENTROPY_THRESHOLD + + echo "... - removing intermediate $IMAGE_TILE ..." +# rm -v $IMAGE_TILE; + + done + echo "..... Removing temporary directory." + rm -rfv magick-tmp + + # moving back to the root of the $SLIDE_NUM directory + cd .. + + echo "..... Collecting all normalized and masked tiles in a file for CellProfiler." +# ls -d -1 $(pwd)/*tiles/ + ls -d -1 $(pwd)/*tiles/*normalized* > files2cp.txt + ls -d -1 $(pwd)/*tiles/ENTROPY* >> files2cp.txt + + fi + + fi + +# ### CellProfiler +# ### This does not work on macOS Mojave 10.14.5 because we need vigra. +# ### However installing this comes with issues: https://github.com/ukoethe/vigra/issues/468 +# ### There is no issue running it on macOS Big Sur 11.5.1+. +# +# echo "" +# echoitalic "* CellProfiler to analyze images. [ $THIS_CELLPROFILER ]" +# +# if [ -z "$(ls -A cp_output)" ]; then +# echo "..... > checking CellProfiler version..." +# cellprofiler --version +# +# echo "..... > making output directory..." +# mkdir -pv cp_output +# +# echo "..... Running CellProfiler using $PIPELINE for [ $SLIDE_NUM ] samples stained with [ $STAIN ]." +# cellprofiler -c -r -p $PIPELINE --file-list files2cp.txt -o cp_output/; +# +# else +# echo "..... CellProfiler was already run, or at least there is a 'cp_output'-directory." +# +# fi +# +# +# ### Wrap Up +# ### 1. we randomly grap 10 masked & tiled PNG-files to keep for post-analysis check >> OUT-COMMENTED +# ### 2. we get the data (in STAIN_Image.txt) for each study number (SLIDE_NUM) in 'cp_output' using >> OUT-COMMENTED +# ### Colsums.R +# ### 3. we remove tiles, and other files we don't need +# echo "" +# echoitalic "* WrapUp the work we have done, and clean house. [ $THIS_WRAPUP ] " +# +# # Randomly grab x (50) overlay images, and remove the rest +# # ls cp_output/*.png | shuf -n $(expr $(ls cp_output/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm; +# +# # Collecting all the data +# echo "..... Creating [ results.txt ] and collecting data." +# echo 'SampleID Slide_number Stain Counts_per_Tissue_area' > results.txt; +# +# # Moving into the cellprofiler output directory for the given $SLIDE_NUM +# cd cp_output; +# +# # parsing SLIDE_NUM "$PWD" +# # cut on . (period), output is 1 +# SAMPLE_NUM=$(echo $SLIDE_NUM | cut -d'.' -f1) +# ### DEBUG +# ### echo "Original slide number: $SLIDE_NUM" +# ### echo "Sample number: $SAMPLE_NUM" +# SCRIPT_NAME="Colsums_${STAIN}.R" +# echo $SAMPLE_NUM $SLIDE_NUM $STAIN $(Rscript $SLIDETOOLKITDIR/utilities/$SCRIPT_NAME $STAIN $OUTPUTFILENAME ) >> ../results.txt; +# ### we used to Gzip +# ### gzip -v ../results.txt; +# head ../results.txt +# cat ../results.txt | wc -l +# # moving up to the $SLIDE_NUM directory again +# cd .. +# +# echo "..... Removing tiling directory and its contents."; +# # Randomly grab x (50) overlay images, and remove the rest +# # ls *tiles/*.png | shuf -n $(expr $(ls *tiles/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm; +# # rm -rfv *tiles/; +# if [ -f *.ndpi ]; then +# echo "..... Removing intermediate tif- & png-files converted from NDPI-files."; +# # rm -v *x40*.tif; +# # rm -v *x40*.png; +# fi; +# +# echo "..... Removing list of files to process."; +# # rm -v files2cp.txt; +# +# echo "..... Gzipping result files."; +# # gzip -vf cp_output/${STAIN}*.gct; +# # gzip -vf cp_output/${STAIN}*.csv; + + echo "" + echo "* Moving back to the project directory [$PROJECTDIR]" + cd $PROJECTDIR + + ### REMOVE THIS BREAK FOR PRODUCTION! + ### When this break is active, only the first item + ### in the list of items to be processed will be processed. + ### + ### OLD CODE + ### INDEX=$((INDEX+1)) + ### break; + + ### NEW CODE + INDEX=$((INDEX+1)) + if [ "$INDEX" -eq "$BATCH_SIZE" ]; then + break; + fi + + # END OF for-loop + done + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message diff --git a/slideQuantify_1_expresshist_mask.sh b/slideQuantify_1_expresshist_mask.sh new file mode 100755 index 0000000..80fb0ce --- /dev/null +++ b/slideQuantify_1_expresshist_mask.sh @@ -0,0 +1,217 @@ +#!/bin/bash +# +# Description: Creates masks for images using slideEMask as part of a slideQuantify +# job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify_1_expresshist_mask [arg1: 210]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: ExpressHIST Masking" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Yipei Song; " +echoitalic " Craig Glastonbury" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-25" +echoitalic "* Version : 1.0.1" +echo "" +echoitalic "* Description : This script will start the masking of images for " +echoitalic " slideToolKit analyses; masking can be adaptive, otsu, graph" +echoitalic " segmentation based." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +EXPRESSHIST="$1" # Depends on arg1 +CONTENTTHRESHOLD="$2" # Depends on arg2 +PATCHSIZE="$3" # Depends on arg3 +OUTPUTDOWN="$4" # Depends on arg4 +MASKMETHOD="$5" # Depends on arg5 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_1_expresshist_mask ***!" + +else + + # Reference + # https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script + SECONDS=0 + # do some work + + # checking if masks exist - if so, skip this script + if [ -f mask_*.png ]; then + echo "..... Masked images already exists - moving on." + exit + fi + + # loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + echo "Masking and creating a tile-crossed image from original image-file." + + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and will be converted to .tif before masking." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-tilecrossed-image *.ndpi + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-tilecrossed-image *.tif + + elif [ -f *.TIF ]; then + echo "The image-file is a .TIF." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-tilecrossed-image *.TIF + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF']" + exit 1 + fi + + echo "..... Masking successfully finished." + + duration=$SECONDS + echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_2_expresshist_tile.sh b/slideQuantify_2_expresshist_tile.sh new file mode 100755 index 0000000..94ce17e --- /dev/null +++ b/slideQuantify_2_expresshist_tile.sh @@ -0,0 +1,212 @@ +#!/bin/bash +# +# Description: Creates masks for images using slideEMask as part of a slideQuantify +# job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify_mask [arg1: 210]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: ExpressHIST Tiling" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Yipei Song; " +echoitalic " Craig Glastonbury" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-25" +echoitalic "* Version : 1.0.0" +echo "" +echoitalic "* Description : This script will start the masking and tiling of images for " +echoitalic " slideToolKit analyses; masking can be adaptive, otsu, graph" +echoitalic " segmentation based; tiling can be any size." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +EXPRESSHIST="$1" # Depends on arg1 +CONTENTTHRESHOLD="$2" # Depends on arg2 +PATCHSIZE="$3" # Depends on arg3 +OUTPUTDOWN="$4" # Depends on arg4 +MASKMETHOD="$5" # Depends on arg5 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_1_imt ***!" + +else + + # Reference + # https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script + SECONDS=0 + # do some work + + # loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + echo "Masking and creating a tile-crossed image from original image-file." + + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and will be converted to .tif before masking and tiling." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-patches --save-tilecrossed-image *.ndpi + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif and tiling." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-patches --save-tilecrossed-image *.tif + + elif [ -f *.TIF ]; then + echo "The image-file is a .TIF and tiling." + + python3 $EXPRESSHIST/pyhist.py --content-threshold $CONTENTTHRESHOLD \ + --patch-size $PATCHSIZE --output-downsample $OUTPUTDOWN --info "verbose" \ + --method "$MASKMETHOD" \ + --save-mask --save-patches --save-tilecrossed-image *.TIF + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF']" + exit 1 + fi + + echo "..... Tiling successfully finished." + + duration=$SECONDS + echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + + + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_3_tile_normalizing.sh b/slideQuantify_3_tile_normalizing.sh new file mode 100755 index 0000000..a3a951f --- /dev/null +++ b/slideQuantify_3_tile_normalizing.sh @@ -0,0 +1,196 @@ +#!/bin/bash +# +# Description: Runs slideNormalize to normalize images as part of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "" + echoerror "- Argument #1 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify_normalizing [arg1: 210]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Normalization" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-09-02" +echoitalic "* Version : 2.0.3" +echo "" +echoitalic "* Description : This script will start the normalization of images for " +echoitalic " slideToolKit analyses." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS + +# Reference +# https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script +SECONDS=0 +# do some work + +# if the list of files to use in CellProfiler exist, we exit this script +if [[ -s files2cp.txt ]] +then + echo "..... Normalization was already applied - moving on." + exit +fi +if [ ! -d *.tiles ]; then + (>&2 echo "*** ERROR *** No tiles to process. Create tiles first using slide2Tiles.") + exit; +fi + +echo "..... Tiles present, starting normalization." +# moving to the required directory +cd *.tiles/; + +# loading required modules +### Loading the CellProfiler-Anaconda3.8 environment +### You need to also have the conda init lines in your .bash_profile/.bashrc file +echo "..... > loading required anaconda environment containing the CellProfiler installation..." +eval "$(conda shell.bash hook)" +conda activate cp4 + +module load slideToolKit +module load slideNormalize + +for IMAGE_TILE in *.tile.tissue.png; do + echo "...Processing tile [ $IMAGE_TILE ]" + echo "... - applying normalization ..." + slideNormalize $IMAGE_TILE; + + echo "... - removing intermediate [ $IMAGE_TILE ] ..." +# rm -v $IMAGE_TILE; + +done + +# moving back to the root of the $SLIDE_NUM directory +cd ../ + +echo "..... Collecting all normalized and masked tiles in a file for CellProfiler." +ls -d -1 $(pwd)/*.tiles/*normalized.tile.tissue.png > files2cp.txt; + +echo "..... Normalizing successfully finished." + +duration=$SECONDS +echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + +script_copyright_message + diff --git a/slideQuantify_4_cellprofiler.sh b/slideQuantify_4_cellprofiler.sh new file mode 100755 index 0000000..7d67c36 --- /dev/null +++ b/slideQuantify_4_cellprofiler.sh @@ -0,0 +1,200 @@ +#!/bin/bash +# +# Description: Runs CellProfiler as part of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #2 -- name of the stain as it appears in the filenames, e.g. FIBRIN. [OPTIONAL]" + echoerror "- Argument #3 -- slidenumber being processed (should contain no spaces). [OPTIONAL]" + echoerror "" + echoerror "An example command would be: slideQuantify_cellprofiler [arg1: path_to_cellprofiler_pipeline] [arg2: STAIN] [arg3: slide_number] " + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: CellProfiler" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-25" +echoitalic "* Version : 2.1.0" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory using CellProfiler *after* " +echoitalic " processing with masking, tiling, and normalizing." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +PIPELINE="$1" # Depends on arg1 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +STAIN=${2-unknown} # Depends on arg2 +SLIDE_NUM=${3-unknown} # Depends on arg2 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_4_cellprofiler ***!" + +else + + # Reference + # https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script + SECONDS=0 + # do some work + + # we check wether there is output already; if so, we exit + if [[ -d cp_output ]]; then + echo "..... CellProfiler was already run, or at least there is a cp_output-directory." + exit + + else + # creating necessary output directory + echo "..... > making output directory..." + mkdir -pv cp_output + + fi + + echo "..... Starting CellProfiler run." + + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + echo "..... > checking CellProfiler version..." + cellprofiler --version + + # running cellprofiler + echo "..... Running CellProfiler using $PIPELINE for [ $SLIDE_NUM ] samples stained with [ $STAIN ]." + cellprofiler -c -r -p $PIPELINE --file-list files2cp.txt -o cp_output/; + + echo "..... CellProfiler successfully finished." + + duration=$SECONDS + echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_5_wrapup.sh b/slideQuantify_5_wrapup.sh new file mode 100755 index 0000000..67e26e1 --- /dev/null +++ b/slideQuantify_5_wrapup.sh @@ -0,0 +1,224 @@ +#!/bin/bash +# +# Description: Wraps up a slideToolKit-CellProfiler analysis for a given slide as part +# of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- output filename where the CellProfiler results are stored, e.g. Image.csv (delimiter is assumed '_')." + echoerror "- Argument #3 -- Random sample. A number to indicate the number of overlay-images after analysis to keep, e.g. '20'." + echoerror "" + echoerror "An example command would be: slideQuantify_wrapup [arg1: STAIN] [arg2: Image.csv] [arg3: RANDOM_SAMPLE] " + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Wrap Up" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-25" +echoitalic "* Version : 2.1.0" +echo "" +echoitalic "* Description : This script will start the wrap up of a slideToolKit analysis." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +OUTPUTFILENAME="$2" # Depends on arg2 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +RANDOM_SAMPLE=${3-50} # Depends on arg11 + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 3 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_wrapup ***!" + +else + + # Reference + # https://stackoverflow.com/questions/8903239/how-to-calculate-time-elapsed-in-bash-script + SECONDS=0 + # do some work + + # Collecting all the data + echo "..... Creating [ results.txt ] and collecting data." + echo 'SampleID Slide_number Stain STAIN_per_Tissue_area' > results.txt; + + # Moving into the cellprofiler output directory for the given $SLIDE_NUM + cd cp_output; + + ### parsing SLIDE_NUM "$PWD" + ### cut on . (period), output is 1 + SLIDE_NUM=$(basename "$PWD") + SAMPLE_NUM=$(basename "$PWD") | cut -d'.' -f1 + ### DEBUG + echo "Original slide number: $SLIDE_NUM" + echo "Sample number: $SAMPLE_NUM" + SCRIPT_NAME="Colsums_${STAIN}.R" + + echo $SAMPLE_NUM $SLIDE_NUM $STAIN $(Rscript $SLIDETOOLKITDIR/utilities/$SCRIPT_NAME $STAIN $OUTPUTFILENAME) >> ../results.txt; + head ../results.txt + cat ../results.txt | wc -l + + # moving up to the $SLIDE_NUM directory again + cd .. + + ### OLD - we may want to keep tiles as these are most time consuming to create + ### echo "..... Removing overlay images:" + ### echo "Randomly grab x (50) overlay images, and remove the rest." + ### ### ls cp_output/*.png | shuf -n $(expr $(ls cp_output/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm -v; + ### + ### echo "..... Removing tiling directory and its contents." + ### echo "Randomly grab x (50) overlay images, and remove the rest" + ### ### ls *tiles/*.png | shuf -n $(expr $(ls *tiles/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm -v; + ### + ### if [ -f *.ndpi ]; then + ### echo "..... Removing intermediate tif- & png-files converted from NDPI-files."; + ### ### We used to work at 40x + ### ### rm -v *x40*.tif; + ### ### rm -v *x40*.png; + ### ### Remember - we work at 20x + ### ### rm -v *x20*.tif; + ### ### rm -v *x20*.png; + ### fi; + + echo "..... Gzipping list of files to process."; + gzip -v files2cp.txt; + + echo "..... Gzipping result files."; + gzip -vf cp_output/${STAIN}*.gct; + gzip -vf cp_output/${STAIN}*.csv; + + echo "..... Wrapping up this slideToolKit run successfully finished." + + duration=$SECONDS + echo "[ $(($duration / 60)) minutes and $(($duration % 60)) seconds elapsed ]" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_cellprofiler.sh b/slideQuantify_cellprofiler.sh new file mode 100755 index 0000000..ca8b776 --- /dev/null +++ b/slideQuantify_cellprofiler.sh @@ -0,0 +1,199 @@ +#!/bin/bash +# +# Description: Runs CellProfiler as part of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- path_to CellProfiler pipeline, e.g. FIBRIN.cppipe." + echoerror "- Argument #2 -- name of the stain as it appears in the filenames, e.g. FIBRIN. [OPTIONAL]" + echoerror "- Argument #3 -- slidenumber being processed (should contain no spaces). [OPTIONAL]" + echoerror "" + echoerror "An example command would be: slideQuantify_cellprofiler [arg1: path_to_cellprofiler_pipeline] [arg2: STAIN] [arg3: slide_number] " + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: CellProfiler" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-09-04" +echoitalic "* Version : 2.0.4" +echo "" +echoitalic "* Description : This script will start the quantification for a given stain" +echoitalic " in a given project directory using CellProfiler *after* " +echoitalic " processing with masking, tiling, and normalizing." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +PIPELINE="$1" # Depends on arg1 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +STAIN=${2-unknown} # Depends on arg2 +SLIDE_NUM=${3-unknown} # Depends on arg2 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_cellprofiler ***!" + +else + + # we check wether there is output already; if so, we exit + if [[ -d cp_output ]] + then + echo "..... CellProfiler was already run, or at least there is a cp_output-directory." + exit + fi + + echo "..... Starting CellProfiler run." + + # loading required module + echo "..... > loading required anaconda module..." + module load anaconda/3-8.2021.05 + + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + echo "..... > checking CellProfiler version..." + cellprofiler --version + + ### DEBUG + ### Direct could be used, and does seem to work + ### cellprofiler="/hpc/local/CentOS7/dhl_ec/software/Anaconda3_2021_05/envs/cp4/bin/cellprofiler" + + # creating necessary output directory + echo "..... > making output directory..." + mkdir -pv cp_output + + # running cellprofiler + echo "..... Running CellProfiler using $PIPELINE for [ $SLIDE_NUM ] samples stained with [ $STAIN ]." + cellprofiler -c -r -p $PIPELINE --file-list files2cp.txt -o cp_output/; + + echo "..... CellProfiler successfully finished" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_mask.sh b/slideQuantify_mask.sh new file mode 100755 index 0000000..9555ed6 --- /dev/null +++ b/slideQuantify_mask.sh @@ -0,0 +1,214 @@ +#!/bin/bash +# +# Description: Creates masks for images using slideEMask as part of a slideQuantify +# job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify_mask [arg1: 210]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Masking" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-23" +echoitalic "* Version : 2.0.7" +echo "" +echoitalic "* Description : This script will start the masking of images for slideToolKit" +echoitalic " analyses." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +EMASKTHRESHOLD="$1" # Depends on arg1 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_mask ***!" + +else + + # checking if masks exist - if so, skip this script + if [ -f *.emask.png ] || [ -f *.mask.png ]; then + echo "..... Masked images already exists - moving on." + exit + fi + + # loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + module load slideToolKit + module load ndpitools + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + echo "Extracting a macro-image from the image-file prior to masking." + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and will be converted to .tif before masking." + ndpisplit -x20 -z0 *.ndpi; + slideMask --layer 0 -f *x20*.tif; + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif." + slideMask --layer 0 -f *x20*.tif; + + elif [ -f *.TIF ]; then + echo "The image-file is a .TIF." + # layer 3 is 20x Roche scanner + # the macro-image is needed for this: slideMask automatically determines this + slideMask -f *.TIF; + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF']" + exit 1 + fi + + echo "Running slideNormalize and slidEMask on the macro-images." +# for MACRO in $(ls *.macro.png); do + echo "... normalizing the macro-images" +# slideNormalize ${MACRO} + slideNormalize *.macro.png; + echo "... entropy masking the normalized macro-images" +# slideEMask -f ${MACRO} -t ${EMASKTHRESHOLD}; + slideEMask -f *.macro.png -t ${EMASKTHRESHOLD}; +# done + + # removing temporary files + echo "..... Removing temporary directory." + rm -rfv magick-tmp + + echo "..... Masking successfully finished" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideQuantify_normalizing.sh b/slideQuantify_normalizing.sh new file mode 100755 index 0000000..2a0c83d --- /dev/null +++ b/slideQuantify_normalizing.sh @@ -0,0 +1,210 @@ +#!/bin/bash +# +# Description: Runs slideNormalize to normalize images as part of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "" + echoerror "- Argument #1 -- eMask threshold. A smaller number is less stringent, best results are obtained using, e.g. '210'." + echoerror "" + echoerror "An example command would be: slideQuantify_normalizing [arg1: 210]" + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Normalization" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-09-02" +echoitalic "* Version : 2.0.3" +echo "" +echoitalic "* Description : This script will start the normalization of images for " +echoitalic " slideToolKit analyses." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +EMASKTHRESHOLD="$1" # Depends on arg1 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 0 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_normalizing ***!" + +else + + # if the list of files to use in CellProfiler exist, we exit this script + if [[ -s files2cp.txt ]] + then + echo "..... Normalization was already applied - moving on." + exit + fi + if [ ! -d *.tiles ]; then + (>&2 echo "*** ERROR *** No tiles to process. Create tiles first using slide2Tiles.") + exit; + fi + + echo "..... Tiles present, starting normalization." + # moving to the required directory + cd *.tiles/; + + # loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + module load slideToolKit + module load slideNormalize + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + for IMAGE_TILE in *.png; do + echo "...Processing tile [ $IMAGE_TILE ]" + echo "... - applying normalization ..." + slideNormalize $IMAGE_TILE; + + echo "... - masking the normalized image ..." + slideEMask -c -f $IMAGE_TILE -t "${EMASKTHRESHOLD}" + + echo "... - removing intermediate $IMAGE_TILE ..." +# rm -v $IMAGE_TILE; + + done + + # removing temporary files + echo "..... Removing temporary directory." + rm -rfv magick-tmp + + # moving back to the root of the $SLIDE_NUM directory + cd .. + + echo "..... Collecting all normalized and masked tiles in a file for CellProfiler." + ls -d -1 $(pwd)/*tiles/*normalized* > files2cp.txt; + ls -d -1 $(pwd)/*tiles/ENTROPY* >> files2cp.txt; + + echo "..... Normalizing successfully finished" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message \ No newline at end of file diff --git a/slideQuantify_tiling.sh b/slideQuantify_tiling.sh new file mode 100755 index 0000000..1a8d0a9 --- /dev/null +++ b/slideQuantify_tiling.sh @@ -0,0 +1,203 @@ +#!/bin/bash +# +# Description: Creates tiles using slide2Tiles after entropy masking as part +# of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "" + echoerror "An example command would be: slideQuantify_tiling [arg1: STAIN] " + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Tiling" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-02" +echoitalic "* Version : 2.0.3" +echo "" +echoitalic "* Description : This script will start the tiling of images for slideToolKit" +echoitalic " analyses." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +# STAIN="$1" # Depends on arg1 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 0 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_tiling ***!" + +else + + # checking if files exist - if so, skip this script + + if [[ -d *.tiles ]] + then + echo "..... Tiles directory already exists - moving on." + exit + fi + + # loading required modules + ### Loading the CellProfiler-Anaconda3.8 environment + ### You need to also have the conda init lines in your .bash_profile/.bashrc file + echo "..... > loading required anaconda environment containing the CellProfiler installation..." + eval "$(conda shell.bash hook)" + conda activate cp4 + + module load slideToolKit + module load ndpitools + + mkdir -pv magick-tmp + export MAGICK_TMPDIR=$(pwd)/magick-tmp + export TMPDIR=$(pwd)/magick-tmp + + if [ -f *.ndpi ]; then + echo "The image-file is a NDPI and should first be converted to .tif before tiling." + ndpisplit -x20 -z0 *.ndpi + slide2Tiles --layer 0 -f *x20*.tif -m *.emask.png; + + elif [ -f *.tif ]; then + echo "The image-file is a (NDPI-converted) .tif." + slide2Tiles --layer 0 -f *x20*.tif -m *.emask.png; + + elif [ -f *.TIF ]; then + # layer 3 is 20x Roche scanner + echo "The image-file is a .TIF." + slide2Tiles --layer 3 -f *.TIF -m *.emask.png; + + else + echoerrorflash "*** ERROR *** Something is rotten in the City of Gotham; most likely a typo. Double back, please. + [image-extension not recognized, should be 'ndpi', 'tif' or 'TIF' ]" + exit 1 + fi + + # removing temporary files + echo "..... Removing temporary directory." + rm -rfv magick-tmp + + echo "..... Tiling successfully finished" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message \ No newline at end of file diff --git a/slideQuantify_wrapup.sh b/slideQuantify_wrapup.sh new file mode 100755 index 0000000..7439a70 --- /dev/null +++ b/slideQuantify_wrapup.sh @@ -0,0 +1,221 @@ +#!/bin/bash +# +# Description: Wraps up a slideToolKit-CellProfiler analysis for a given slide as part +# of a slideQuantify job-session. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- output filename where the CellProfiler results are stored, e.g. Image.csv (delimiter is assumed '_')." + echoerror "- Argument #3 -- Random sample. A number to indicate the number of overlay-images after analysis to keep, e.g. '20'." + echoerror "" + echoerror "An example command would be: slideQuantify_wrapup [arg1: STAIN] [arg2: Image.csv] [arg3: RANDOM_SAMPLE] " + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideQuantify: Wrap Up" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic " Yipei Song" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-12" +echoitalic "* Version : 2.0.4" +echo "" +echoitalic "* Description : This script will start the wrap up of a slideToolKit analysis." +echoitalic " This is SLURM based." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +OUTPUTFILENAME="$2" # Depends on arg2 + +### OPTIONAL | GENERALS +### https://stackoverflow.com/questions/9332802/how-to-write-a-bash-script-that-takes-optional-input-arguments +RANDOM_SAMPLE=${3-50} # Depends on arg11 + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 3 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideQuantify_wrapup ***!" + +else + + ### DEBUG + ### SBATCH --output=slidemask_out_%j.log # Standard output and error log + + # Collecting all the data + echo "..... Creating [ results.txt ] and collecting data." + echo 'SampleID Slide_number Stain STAIN_per_Tissue_area' > results.txt; + + # Moving into the cellprofiler output directory for the given $SLIDE_NUM + cd cp_output; + + # parsing SLIDE_NUM "$PWD" + # cut on . (period), output is 1 + SLIDE_NUM=$(basename "$PWD") + SAMPLE_NUM=$(basename "$PWD") | cut -d'.' -f1 + ### DEBUG + ### echo "Original slide number: $SLIDE_NUM" + ### echo "Sample number: $SAMPLE_NUM" + SCRIPT_NAME="Colsums_${STAIN}.R" + + echo $SAMPLE_NUM $SLIDE_NUM $STAIN $(Rscript $SLIDETOOLKITDIR/utilities/$SCRIPT_NAME $STAIN $OUTPUTFILENAME) >> ../results.txt; + ### we used to Gzip + ### gzip -v ../results.txt; + head ../results.txt + cat ../results.txt | wc -l + + # moving up to the $SLIDE_NUM directory again + cd .. + + echo "..... Removing overlay images:" + echo "Randomly grab x (50) overlay images, and remove the rest." + ### ls cp_output/*.png | shuf -n $(expr $(ls cp_output/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm -v; + + echo "..... Removing tiling directory and its contents." + echo "Randomly grab x (50) overlay images, and remove the rest" + ### ls *tiles/*.png | shuf -n $(expr $(ls *tiles/*.png | wc -l) - $RANDOM_SAMPLE) | xargs rm -v; + + if [ -f *.ndpi ]; then + echo "..... Removing intermediate tif- & png-files converted from NDPI-files."; + ### We used to work at 40x + ### rm -v *x40*.tif; + ### rm -v *x40*.png; + ### Remember - we work at 20x + ### rm -v *x20*.tif; + ### rm -v *x20*.png; + + fi; + + echo "..... Gzipping list of files to process."; + gzip -v files2cp.txt; + + echo "..... Gzipping result files."; + gzip -vf cp_output/${STAIN}*.gct; + gzip -vf cp_output/${STAIN}*.csv; + + echo "..... Wrapping up this slideToolKit run successfully finished" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + diff --git a/slideRename b/slideRename index 19f46c7..c6a71b9 100755 --- a/slideRename +++ b/slideRename @@ -3,7 +3,8 @@ # Description: Batch rename slides # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -77,8 +78,8 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. @@ -97,6 +98,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -164,10 +167,12 @@ Code). Image manipulation can be done befor previewing the slide using the --convert-args flag, the equal sign ('=') and surrounding quotes "'" are required. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } @@ -380,6 +385,7 @@ checkDependencies(){ fi } # show thumbs per scanner type (-filter box is best for resizing and text) +# layer information can be determined using example files (http://openslide.cs.cmu.edu/download/openslide-testdata/) getThumb(){ # get thumb by layer id LAYER="$LAYER" @@ -399,7 +405,7 @@ getThumbLeica(){ convert "$FILEFULL[$LAYER]" -rotate 180 -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" } getThumbHamamatsu(){ - LAYER=10 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + LAYER=7 # determined from example file from our in-house (UMC Utrecht) Hamamatsu scanner convert "$FILEFULL[$LAYER]" -rotate 90 -filter box -resize "$RESIZE" $IM_ARGS "$PREVIEW_TMPFILE" } getThumbiScan(){ diff --git a/slideRename.py b/slideRename.py new file mode 100755 index 0000000..66eaa34 --- /dev/null +++ b/slideRename.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python +# script to display a whole-slide image file and manually rename the file (*.TIF, *.NDPI, etc.) +# +# Ref: https://github.com/choosehappy/Snippets/blob/master/extract_macro_level_from_wsi_image_openslide_cli.py +# + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideRename: display and manually rename images ") +print("") +print("* Version : v1.0.2") +print("") +print("* Last update : 2022-09-07") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Inspired by : choosehappy | https://github.com/choosehappy") +print("") +print("* Description : This script will get thumbnails from (a list of given) images. The user can hit a key and") +print(" manually type in the correct file name. If the new filename already exists in that ") +print(" directory the renaming will be cancelled.") +print(" Note: if the renamed file already exists, an error will occur. Also refer to the ") +print(" StackOverflow post:") +print(" https://stackoverflow.com/questions/30175369/python-program-to-rename-file-names-while-overwriting-if-there-already-is-that-f") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import numpy as np +import glob +from pathlib import Path + +# for argument parser +from sys import exit, argv +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +parser = argparse.ArgumentParser( + prog='slideRename', + description='This script will display thumbnails from given images and a terminal window for manual renaming images.', +# usage='slideRename -i/--input; optional: -d/--display -o/--outdir -s/--suffix -f/--force; for help: -h/--help', + usage='slideRename -i/--input; optional: -o/--outdir -s/--suffix -f/--force; for help: -h/--help', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2022 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) + +# parser.add_argument('-d', '--display', help="Only shows a Thumb on display, no renaming takes place.", action="store_true") +parser.add_argument('-o', '--outdir', help="Output dir, default is the input image(s) directory.", default="./", type=str) +parser.add_argument('-s', '--suffix', help="Suffix to append to end of file, no suffix will be added by default.", default="", type=str) +parser.add_argument('-f', '--force', help="Force output even if it exists.", default=False, action="store_true") + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument('-i','--input',help="An input image (or directory containing image files) should be given. Try: IMG012.ndpi, *.TIF or /path_to/images/*.ndpi.", nargs="*") + +args = parser.parse_args() + +if not args.input: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideRename ***!") + print("Note that -i/--input is required, i.e. an input image (or directory containing image files) should be given. Try: IMG012.ndpi, *.TIF or /path_to/images/*.ndpi.\n") + parser.print_help() + exit() + +if len(args.input) > 1: # bash has sent us a list of files + files = args.input +else: # user sent us a wildcard, need to use glob to find files + files = glob.glob(args.input[0]) + +# make the output directory if it does not exist +if not os.path.exists(args.outdir): + os.makedirs(args.outdir) + +# if args.display: +# print("Displaying [",fname,"] at level [",level,"].") +# # Let's print each dimension of the image +# print('* image dimensions (height x width in pixels):', img.shape) +# ### FOR DEBUG +# # print('Image data type:', img.dtype) +# # print('Image type:', type(img)) +# img_size = img.size/1024 # to get kilobytes +# print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb +# +# # To display our image variable, we use 'imshow' +# # The first parameter will be title shown on image window +# # The second parameter is the image variable +# # rotate the image for easy reading (https://www.geeksforgeeks.org/python-opencv-cv2-rotate-method/) +# img_r = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) +# cv2.imshow(print('Display image [',fname,'] at level [',level,']'), cv2.cvtColor(img_r, cv2.COLOR_RGB2BGR)) +# print('(hit any key on the image to close)') # how waitKey works +# +# # waitKey - ref: https://stackoverflow.com/questions/22274789/cv2-imshow-function-is-opening-a-window-that-always-says-not-responding-pyth +# # 'waitKey' allows us to wait for a key stroke +# # when a image window is open +# # By leaving it blank it just waits for any key to be +# # pressed before continuing. +# # By placing numbers (except 0), we can specify a delay for +# # how long you keep the window open (time is in millisecs here) +# cv2.waitKey() +# # This closes all open windows +# # Failure to place this will cause your program to hang +# cv2.destroyAllWindows() +# cv2.waitKey(1) +# else: +for fname in files: + + # this will get the file with the path: os.path.splitext + # this will get the file name: os.path.basename + # the [0] will get the first part of the multiple parts + fname_base = os.path.splitext(os.path.basename(fname))[0] + fname_base_ext = os.path.splitext(os.path.basename(fname))[1] + + if not args.outdir: + print("Output directory was not given, set to [",os.path.dirname(fname),"].") + fname_base_dir = os.path.dirname(fname) + else: + print("Output directory was given, set to [",args.outdir,"].") + fname_base_dir = args.outdir + + fimage=openslide.OpenSlide(fname) + + img = fimage.associated_images["macro"] # macro will get the color thumbnail from an image + img = np.asarray(img)[:,:, 0:3] + img_r = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + + print("Displaying [",fname_base,"] at path [",fname_base_dir,"] with extension [",fname_base_ext,"].") + + # Let's print each dimension of the image + print('* image dimensions (height x width in pixels):', img.shape) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + + # To display our image variable, we use 'imshow' + # The first parameter will be title shown on image window + # The second parameter is the image variable + # rotate the image for easy reading (https://www.geeksforgeeks.org/python-opencv-cv2-rotate-method/) + print('Display image [',fname_base,']') + # don't use 'fname_base' as first variable as this will create a new window for each + # new slide (so you'll get multiiple open windows) + cv2.imshow('slidePreview', cv2.cvtColor(img_r, cv2.COLOR_RGB2BGR)) + + # waitKey - ref: https://stackoverflow.com/questions/22274789/cv2-imshow-function-is-opening-a-window-that-always-says-not-responding-pyth + # 'waitKey' allows us to wait for a key stroke + # when a image window is open + # By leaving it blank it just waits for any key to be + # pressed before continuing. + # By placing numbers (except 0), we can specify a delay for + # how long you keep the window open (time is in millisecs here) + # By placing 1 this will cause the window to open, but immediately move to the next + # command/instruction in this script + cv2.waitKey(1) + + # Rename file + # here the user can manually rename the file + # ask for filename if non is existing (by typing or by barcode) + # This is bash-code and needs to be edited to python + old_fname = fname_base + + new_fname = input("Enter slide name: ") + print("You entered: " + new_fname) + + old_fnameout=f"{fname_base_dir}/{old_fname}{fname_base_ext}" + new_fnameout=f"{fname_base_dir}/{new_fname}{fname_base_ext}" + + # This closes all open windows + # Failure to place this will cause your program to hang + cv2.destroyAllWindows() + cv2.waitKey(1) + + if not args.force and os.path.exists(new_fnameout): + print(f"Skipping {new_fnameout} as output file exists and --force is not set") + continue + + print("Processing [",fname,"].") + print('* image dimensions (height x width in pixels):', img.shape) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + os.rename(old_fnameout, new_fnameout) + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2022 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/slideReset b/slideReset new file mode 100755 index 0000000..1e9913d --- /dev/null +++ b/slideReset @@ -0,0 +1,34 @@ +#!/bin/bash +# +# Description: Reset analysis directory: delete everything except the input image-file. +# +# The MIT License (MIT) +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. +# +# Permission is hereby granted, free of charge, to any person obtaining a +# copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +# CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +# + +rm -rfv AE*/*tiles +rm -rfv AE*/cp_output +rm -v AE*/result.txt +rm -v AE*/files2cp.txt +rm -v errors/* +rm -v logs/* diff --git a/slideSQLheader b/slideSQLheader index d4bb90a..a792316 100755 --- a/slideSQLheader +++ b/slideSQLheader @@ -3,7 +3,8 @@ # Description: Extract SQL-file column headers created by cellprofiler # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -41,8 +42,8 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. @@ -61,6 +62,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -98,10 +101,12 @@ Extract SQL-file column names created by cellprofiler. These headers can be added to a .csv file using the --add-head option, a copy (.head.csv) will be created -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } #Menu diff --git a/slideThumb b/slideThumb index 551bfbf..0c69e8d 100755 --- a/slideThumb +++ b/slideThumb @@ -3,7 +3,8 @@ # Description: Create slide thumbnail, including label # # The MIT License (MIT) -# Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +# Copyright (c) 2014-2021, Bas G.L. Nelissen, Sander W. van der Laan, +# UMC Utrecht, Utrecht, the Netherlands. # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the @@ -34,6 +35,7 @@ DEFAULT_OUTPUT_SUFFIX=".thumb" DEFAULT_OUTPUT_EXTENSION="png" DEFAULT_OUTPUT_DIRECTORY="." # Relative to input file DEFAULT_RESIZE="1024x1024" +DEFAULT_IM_ARGS="" # convert arguments # -verbose # sort arrays DEPENDENCIES=( $( for el in "${DEPENDENCIES[@]}"; do echo "$el"; done | sort) ) SUPPORTED_FORMATS=( $( for el in "${SUPPORTED_FORMATS[@]}"; do echo "$el"; done | sort) ) @@ -53,11 +55,11 @@ EOF # version info version() { cat << EOF -slideToolkit 0.1 beta -($SCRIPTNAME is part of the slideToolkit) +slideToolKit v1.2 +($SCRIPTNAME is part of the slideToolKit) The MIT License (MIT) -Copyright (c) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. +Copyright (c) 2014-2018, Bas G.L. Nelissen & Sander W. van der Laan, UMC Utrecht, Utrecht, the Netherlands. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the @@ -73,6 +75,8 @@ in all copies or substantial portions of the Software. There is NO WARRANTY, to the extent permitted by law. Written by Bas G.L. Nelissen | https://github.com/bglnelissen. +Edits by Sander W. van der Laan | https://github.com/swvanderlaan. + EOF } @@ -93,6 +97,7 @@ options: [--extension] thumbnail output extension ["$DEFAULT_OUTPUT_EXTENSION"] [--output-dir] path to output directory [--resize] maximum output dimentions ["$DEFAULT_RESIZE"] + [--convert-args]='' convert arguments ('=' sign and 'quotes' required) [--ignore-dependencies] ignores all dependencies, but gives warning --help display this help @@ -125,10 +130,12 @@ directory can be set with --output-dir (path can be a relative path or absolute path, if the location does not exist it will be created). Pipe the 'job list' output in 'parallel' for parallel processing. -The slideToolkit and all its tools are released under the terms of the MIT license. -The slideToolkit (C) 2014-2016, Bas G.L. Nelissen, UMC Utrecht, the Netherlands. -Report issues at https://github.com/bglnelissen/slideToolkit/issues. +The slideToolKit and all its tools are released under the terms of the MIT license. +The slideToolKit (C) 2014-2021, Bas G.L. Nelissen, UMC Utrecht, the Netherlands; +Sander W. van der Laan | s.w.vanderlaan-2 [at] gmail [dot] com, UMC Utrecht, +the Netherlands. +Report issues at https://github.com/swvanderlaan/slideToolKit/issues. EOF } @@ -196,7 +203,10 @@ do --output-dir=*) OUTPUT_DIRECTORY=${1#*=} shift ;; - --ignore-dependencies) + --convert-args=*) # Equal sign and quotes are required for this option. If not, the convert arguments are seen as slideRename options. + IM_ARGS=${1#*=} + shift ;; + --ignore-dependencies) IGNOREDEPENDENCIES=TRUE shift ;; --) # End of all options @@ -241,6 +251,12 @@ if [ "$RESIZE" != "" ]; then else RESIZE="$DEFAULT_RESIZE" fi +# set IM_ARGS default +if [ "$IM_ARGS" != "" ]; then + IM_ARGS="$IM_ARGS" +else + IM_ARGS="$DEFAULT_IM_ARGS" +fi # set IGNOREDEPENDENCIES (this if statement does nothing, as it is set to set in Menu) if [[ "$IGNOREDEPENDENCIES" =~ (true|TRUE|YES|yes) ]] ; then IGNOREDEPENDENCIES=true @@ -282,11 +298,11 @@ checkDependencies(){ getThumb(){ # get thumb by layer id LAYER="$LAYER" - convert "$FILEFULL[$LAYER]" -resize "$RESIZE" "$OUTPUTFILENAME" + convert $IM_ARGS "$FILEFULL[$LAYER]" -resize "$RESIZE" "$OUTPUTFILENAME" } getThumbiScanHT(){ LAYER=0 - convert "$FILEFULL[$LAYER]" -resize "$RESIZE" "$OUTPUTFILENAME" + convert $IM_ARGS "$FILEFULL[$LAYER]" -resize "$RESIZE" "$OUTPUTFILENAME" } getThumbAperio(){ # the last 2 layers contain thumb and the tissue (couning starts at 0) @@ -294,19 +310,19 @@ getThumbAperio(){ N_LABEL="$(($NR_LAYERS - 2))" N_TISSUE="$(($NR_LAYERS - 1))" # get label, get tissue, rotate one and resize both, then append and do a final resize - convert "$FILEFULL[$N_LABEL]" -resize 1024x \( "$FILEFULL[$N_TISSUE]" -rotate 90 -resize 1024x \)-append -resize "$RESIZE" "$OUTPUTFILENAME" + convert $IM_ARGS "$FILEFULL[$N_LABEL]" -resize 1024x \( "$FILEFULL[$N_TISSUE]" -rotate 90 -resize 1024x \)-append -resize "$RESIZE" "$OUTPUTFILENAME" } getThumbLeica(){ LAYER=1 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) - convert "$FILEFULL"[$LAYER] -rotate 180 -resize "$RESIZE" "$OUTPUTFILENAME" + convert $IM_ARGS "$FILEFULL"[$LAYER] -rotate 180 -resize "$RESIZE" "$OUTPUTFILENAME" } getThumbHamamatsu(){ - LAYER=10 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) - convert "$FILEFULL"[$LAYER] -rotate 90 -resize "$RESIZE" "$OUTPUTFILENAME" + LAYER=6 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) + convert $IM_ARGS "$FILEFULL"[$LAYER] -rotate 90 -resize "$RESIZE" "$OUTPUTFILENAME" } getThumbiScan(){ LAYER=0 # determined from example file (http://openslide.cs.cmu.edu/download/openslide-testdata/) - convert "$FILEFULL"[$LAYER] -resize "$RESIZE" "$OUTPUTFILENAME" + convert $IM_ARGS "$FILEFULL"[$LAYER] -resize "$RESIZE" "$OUTPUTFILENAME" } programOutput(){ # set variables diff --git a/slideThumb.py b/slideThumb.py new file mode 100755 index 0000000..388028d --- /dev/null +++ b/slideThumb.py @@ -0,0 +1,144 @@ +#!/usr/bin/env python +# script to extract a thumbnails (and display) whole-slide image files (*.TIF, *.NDPI, etc.) +# +# Ref: https://github.com/choosehappy/Snippets/blob/master/extract_macro_level_from_wsi_image_openslide_cli.py +# + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideThumb: extract thumbnails images ") +print("") +print("* Version : v1.0.2") +print("") +print("* Last update : 2022-06-09") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Inspired by : choosehappy | https://github.com/choosehappy") +print("") +print("* Description : This script will get thumbnails from (a list of given) images for quick inspection.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import numpy as np +import glob +from pathlib import Path + +# for argument parser +from sys import exit, argv +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +parser = argparse.ArgumentParser( + prog='slideThumb', + description='This script will get thumbnails from given images for quick inspection.', + usage='slideThumb -i/--input; optional: -d/--display -o/--outdir -s/--suffix -t/--type -f/--force -v/--verbose; for help: -h/--help', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) + +parser.add_argument('-d', '--display', help="Also show Thumbnail on display, default simply writes thumbnails.", action="store_true") +parser.add_argument('-o', '--outdir', help="Output dir, default is present working directory.", default="./", type=str) +parser.add_argument('-s', '--suffix', help="Suffix to append to end of file, default is 'thumb' for thumbnail.", default="", type=str) +parser.add_argument('-t', '--type', help="Output file type, default is png (which is slower), other options are tif.", default="png", type=str) +parser.add_argument('-f', '--force', help="Force utput even if it exists.", default=False, action="store_true") +parser.add_argument('-v', '--verbose', help="While writing images also display image properties.", default=False, action="store_true") + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument('-i','--input',help="Input (directory containing files). Try: *.TIF or /path_to/images/*.ndpi.", nargs="*") + +args = parser.parse_args() + +if not args.input: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideThumb ***!") + print("Note that -i/--input is required. Try: *.TIF or /path_to/images/*.ndpi.\n") + parser.print_help() + exit() + +if len(args.input) > 1: # bash has sent us a list of files + files = args.input +else: # user sent us a wildcard, need to use glob to find files + files = glob.glob(args.input[0]) + +# make the output directory if it does not exist +if not os.path.exists(args.outdir): + os.makedirs(args.outdir) + +for fname in files: + fnameout=f"{Path(fname).stem}{args.suffix}.!!.{args.type if args.type[0] == '' else ''+args.type}" + fnameout=Path(args.outdir,fnameout) + + if not args.force and os.path.exists(fnameout): + print(f"Skipping {fnameout} as output file exists and --force is not set") + continue + + fimage=openslide.OpenSlide(fname) + + img = fimage.associated_images["macro"] # macro will get the color thumbnail from an image + img = np.asarray(img)[:,:, 0:3] + img_r = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE) + + if args.display: + print("Displaying [",fname,"].") + # Let's print each dimension of the image + print('* image dimensions (height x width in pixels):', img.shape) + ### FOR DEBUG +# print('Image data type:', img.dtype) +# print('Image type:', type(img)) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + + # To display our image variable, we use 'imshow' + # The first parameter will be title shown on image window + # The second parameter is the image variable + # rotate the image for easy reading (https://www.geeksforgeeks.org/python-opencv-cv2-rotate-method/) + cv2.imshow(print('Display image [',fname,']'), cv2.cvtColor(img_r, cv2.COLOR_RGB2BGR)) + print('(hit any key on the image to close)') # how waitKey works + + # waitKey - ref: https://stackoverflow.com/questions/22274789/cv2-imshow-function-is-opening-a-window-that-always-says-not-responding-pyth + # 'waitKey' allows us to wait for a key stroke + # when a image window is open + # By leaving it blank it just waits for any key to be + # pressed before continuing. + # By placing numbers (except 0), we can specify a delay for + # how long you keep the window open (time is in millisecs here) + cv2.waitKey() + # This closes all open windows + # Failure to place this will cause your program to hang + cv2.destroyAllWindows() + cv2.waitKey(1) + elif args.verbose: + print("Processing [",fname,"].") + print('* image dimensions (height x width in pixels):', img.shape) + img_size = img.size/1024 # to get kilobytes + print('* image size:', '{:,.2f}'.format(img_size), 'KB') # to get Kb + cv2.imwrite(str(fnameout).replace("!!","thumb"),cv2.cvtColor(img_r,cv2.COLOR_RGB2BGR)) + else: + print("Writing thumbnail for [",fname,"].") + cv2.imwrite(str(fnameout).replace("!!","thumb"),cv2.cvtColor(img_r,cv2.COLOR_RGB2BGR)) + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") diff --git a/slideToolKitTest.py b/slideToolKitTest.py new file mode 100755 index 0000000..8e97087 --- /dev/null +++ b/slideToolKitTest.py @@ -0,0 +1,92 @@ +#!/usr/bin/python +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideToolKitTest: test the environment ") +print("") +print("* Version : v1.0.0") +print("") +print("* Last update : 2021-11-06") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("") +print("* Description : This script will test the slideToolKit virtual environment.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# import required packages +import sys +import time +import arrow +import datetime + +# for argument parser +import argparse +import textwrap + +# for openslide/openCV +import cv2 +import os +import openslide +from openslide import * + +# for cellprofiler +import cellprofiler + +utc = arrow.utcnow() + +# argument parser +parser = argparse.ArgumentParser( + prog='slideToolKitTest', + description='This script will test the slideToolKit virtual environment.', + usage='slideToolKitTest [-h/--help] -b/--bdate.', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 1979-2021 Sander W. van der Laan | s.w.vanderlaan@gmail.com")) +parser.add_argument('-v', '--verbose', help="Will get all available image properties.", default=False, action="store_true") +parser.add_argument('-b','--bdate',help="Your birthdate - format YYYY-MM-DD. Try: 1979-01-13.", +# required=True, + type=datetime.date.fromisoformat) # make sure that the date format is correct, if not the program will stop + +args = parser.parse_args() + +if not args.bdate: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideToolKitTest ***!") + print("Note that -b/--bdate is required. Try: 1979-01-13.\n") + parser.print_help() + exit() + +bd = args.bdate +print("Printing the installed versions.") +print('* Python version: ',sys.version) +print('* OpenSlide version: ',openslide.__version__) +print('* OpenSlide library version: ', openslide.__library_version__) +print('* CellProfiler version: ', cellprofiler.__version__) + +print("\nTesting the argument parsing of your birth date.") +print(f"UTC time is = {utc}") +pacific = utc.to('Europe/Vatican') #https://pvlib-python.readthedocs.io/en/stable/timetimezones.html +print(f"European/Amsterdam time is = {pacific}") +bdate_text='You were born on ' + str(bd) # make a string out of the date +birth_date = arrow.get(bdate_text, 'YYYY-MM-DD') # arrow() will get the date from a text +print(f"Your birth date is {birth_date}.") +print(f"You were born {birth_date.humanize()}.") + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + diff --git a/upgrade_study/GLYCC/summary/0_screenshot.png b/upgrade_study/GLYCC/summary/0_screenshot.png new file mode 100644 index 0000000..e6c4972 Binary files /dev/null and b/upgrade_study/GLYCC/summary/0_screenshot.png differ diff --git a/upgrade_study/GLYCC/summary/1_1_colortogray_nr5_cycle1_combined.pdf b/upgrade_study/GLYCC/summary/1_1_colortogray_nr5_cycle1_combined.pdf new file mode 100644 index 0000000..d380361 Binary files /dev/null and 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+Pipeline_Pipeline,"CellProfiler Pipeline: http://www.cellprofiler.org +Version:5 +DateRevision:413 +GitHash: +ModuleCount:14 +HasImagePlaneDetails:False + +Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + : + Filter images?:Custom + Select the rule criteria:and (file does contain "".normalized.tile.tissue.png"") (file doesnot contain ""ENTROPY_"") + +Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Extract metadata?:Yes + Metadata data type:Text + Metadata types:{} + Extraction method count:1 + Metadata extraction method:Extract from file/folder names + Metadata source:File name + Regular expression to extract from file name:^(?PAE\d{1,4}).?(?P(UMC|T.*?))?.(?PHE|HE_x\d+\_z\d+).?(?P[0-9]{4}[0-9]{2}[0-9]{2})?.(?PX\d+).(?PY\d+).*$ + Regular expression to extract from folder name:(?P[0-9]{4}_[0-9]{2}_[0-9]{2})$ + Extract metadata from:Images matching a rule + Select the filtering criteria:and (file doesnot contain "".counted"") (file does contain "".normalized.tile.tissue.png"") + Metadata file location:Elsewhere...| + Match file and image metadata:[] + Use case insensitive matching?:No + Metadata file name: + Does cached metadata exist?:No + +NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Assign a name to:All images + Select the image type:Color image + Name to assign these images:Original + Match metadata:[{u'Original': u'Y', u'EntropyImage': u'Y'}, {u'Original': u'X', u'EntropyImage': u'X'}, {u'Original': u'NR', u'EntropyImage': u'NR'}, {u'Original': u'STAIN', u'EntropyImage': u'STAIN'}] + Image set matching method:Metadata + Set intensity range from:Image metadata + Assignments count:2 + Single images count:0 + Maximum intensity:255.0 + Process as 3D?:No + Relative pixel spacing in X:1.0 + Relative pixel spacing in Y:1.0 + Relative pixel spacing in Z:1.0 + Select the rule criteria:and (file doesnot contain ""ENTROPY_"") (file doesnot contain "".counted"") (file does contain "".tile.tissue.png"") + Name to assign these images:Original + Name to assign these objects:Cell + Select the image type:Color image + Set intensity range from:Image metadata + Maximum intensity:255.0 + Select the rule criteria:and (file does contain ""ENTROPY_"") + Name to assign these images:EntropyImage + Name to assign these objects:Nucleus + Select the image type:Binary mask + Set intensity range from:Image metadata + Maximum intensity:255.0 + +Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Do you want to group your images?:No + grouping metadata count:1 + Metadata category:None + +ColorToGray:[module_num:5|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Create gray channel, this is used to find tissue (green).', 'Red is ussually a good channel for tissue.', 'RGB should also be fine.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Original + Conversion method:Combine + Image type:RGB + Name the output image:OrigGray + Relative weight of the red channel:1.0 + Relative weight of the green channel:1.0 + Relative weight of the blue channel:1.0 + Convert red to gray?:Yes + Name the output image:OrigRed + Convert green to gray?:Yes + Name the output image:OrigGreen + Convert blue to gray?:Yes + Name the output image:OrigBlue + Convert hue to gray?:Yes + Name the output image:OrigHue + Convert saturation to gray?:Yes + Name the output image:OrigSaturation + Convert value to gray?:Yes + Name the output image:OrigValue + Channel count:1 + Channel number:1 + Relative weight of the channel:1.0 + Image name:Channel1 + +Smooth:[module_num:6|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Smooth away the dust using a Gaussian Filter. This is used to find Tissue (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:OrigGray + Name the output image:ImageAfterMath + Select smoothing method:Gaussian Filter + Calculate artifact diameter automatically?:No + Typical artifact diameter:20.0 + Edge intensity difference:0.1 + Clip intensities to 0 and 1?:Yes + +ImageMath:[module_num:7|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Invert the gray image, black becomes white, white becomes black.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Operation:Invert + Raise the power of the result by:1.0 + Multiply the result by:1.0 + Add to result:0.0 + Set values less than 0 equal to 0?:Yes + Set values greater than 1 equal to 1?:Yes + Replace invalid values with 0?:Yes + Ignore the image masks?:No + Name the output image:Tissue_object + Image or measurement?:Image + Select the first image:ImageAfterMath + Multiply the first image by:1.0 + Measurement: + Image or measurement?:Image + Select the second image: + Multiply the second image by:1.0 + Measurement: + +IdentifyPrimaryObjects:[module_num:8|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total tissue area (green).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:Tissue_object + Name the primary objects to be identified:Tissue + Typical diameter of objects, in pixel units (Min,Max):50,10000 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:No + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:None + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Manual + Threshold smoothing scale:1.3488 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.0,1.0 + Manual threshold:0.065 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:No + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Otsu + +UnmixColors:[module_num:9|svn_version:'Unknown'|variable_revision_number:2|show_window:True|notes:['Unmix the colors. Hematoxylin (blueish for nuclei) and Eosin (pinkish for tissue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Stain count:2 + Select the input color image:Original + Name the output image:HE + Stain:Hematoxylin + Red absorbance:0.434522 + Green absorbance:0.757134 + Blue absorbance:0.487789 + Name the output image:Eosin + Stain:Eosin + Red absorbance:0.5 + Green absorbance:0.5 + Blue absorbance:0.5 + +IdentifyPrimaryObjects:[module_num:10|svn_version:'Unknown'|variable_revision_number:14|show_window:True|notes:['Identify total HE area (blue).']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the input image:HE + Name the primary objects to be identified:HE_Nuclei + Typical diameter of objects, in pixel units (Min,Max):12,36 + Discard objects outside the diameter range?:Yes + Discard objects touching the border of the image?:Yes + Method to distinguish clumped objects:Intensity + Method to draw dividing lines between clumped objects:Intensity + Size of smoothing filter:10 + Suppress local maxima that are closer than this minimum allowed distance:7.0 + Speed up by using lower-resolution image to find local maxima?:Yes + Fill holes in identified objects?:After both thresholding and declumping + Automatically calculate size of smoothing filter for declumping?:Yes + Automatically calculate minimum allowed distance between local maxima?:Yes + Handling of objects if excessive number of objects identified:Continue + Maximum number of objects:500 + Display accepted local maxima?:No + Select maxima color:Blue + Use advanced settings?:Yes + Threshold setting version:12 + Threshold strategy:Global + Thresholding method:Otsu + Threshold smoothing scale:0 + Threshold correction factor:1.0 + Lower and upper bounds on threshold:0.70,1.0 + Manual threshold:0.0 + Select the measurement to threshold with:None + Two-class or three-class thresholding?:Two classes + Log transform before thresholding?:Yes + Assign pixels in the middle intensity class to the foreground or the background?:Foreground + Size of adaptive window:10 + Lower outlier fraction:0.05 + Upper outlier fraction:0.05 + Averaging method:Mean + Variance method:Standard deviation + # of deviations:2 + Thresholding method:Minimum Cross-Entropy + +OverlayOutlines:[module_num:11|svn_version:'Unknown'|variable_revision_number:4|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (blue)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Display outlines on a blank image?:No + Select image on which to display outlines:Original + Name the output image:OrigOverlay + Outline display mode:Color + Select method to determine brightness of outlines:Max of image + How to outline:Inner + Select outline color:#118002 + Select objects to display:Tissue + Select outline color:#0000FF + Select objects to display:HE_Nuclei + +SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:15|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the type of image to save:Image + Select the image to save:OrigOverlay + Select method for constructing file names:From image filename + Select image name for file prefix:Original + Enter single file name:OrigBlue + Number of digits:4 + Append a suffix to the image file name?:Yes + Text to append to the image name:.counted + Saved file format:png + Output file location:Default Output Folder| + Image bit depth:8-bit integer + Overwrite existing files without warning?:Yes + When to save:Every cycle + Record the file and path information to the saved image?:No + Create subfolders in the output folder?:No + Base image folder:Elsewhere...| + How to save the series:T (Time) + +MeasureImageAreaOccupied:[module_num:13|svn_version:'Unknown'|variable_revision_number:5|show_window:True|notes:['Measure total tissue area (green) and total HE positive stain (red)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Measure the area occupied by:Objects + Select binary images to measure:Eosin, HE, OrigGray + Select object sets to measure:HE_Nuclei, Tissue + +ExportToSpreadsheet:[module_num:14|svn_version:'Unknown'|variable_revision_number:13|show_window:True|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False] + Select the column delimiter:Comma ("","") + Add image metadata columns to your object data file?:Yes + Add image file and folder names to your object data file?:No + Select the measurements to export:No + Calculate the per-image mean values for object measurements?:Yes + Calculate the per-image median values for object measurements?:Yes + Calculate the per-image standard deviation values for object measurements?:Yes + Output file location:Default Output Folder| + Create a GenePattern GCT file?:Yes + Select source of sample row name:Metadata + Select the image to use as the identifier:None + Select the metadata to use as the identifier:FileName_Original + Export all measurement types?:Yes + Press button to select measurements:Image|Metadata_FileLocation,Image|Metadata_T_NUMBER,Image|Metadata_X,Image|Metadata_Series,Image|Metadata_STAIN,Image|Metadata_Date,Image|Metadata_NR,Image|Metadata_Y,Image|Metadata_Frame,Image|Width_Original,Image|MD5Digest_Original,Image|AreaOccupied_Perimeter_HE_Nuclei,Image|AreaOccupied_Perimeter_Tissue,Image|AreaOccupied_TotalArea_HE_Nuclei,Image|AreaOccupied_TotalArea_Tissue,Image|AreaOccupied_AreaOccupied_HE_Nuclei,Image|AreaOccupied_AreaOccupied_Tissue,Image|Frame_Original,Image|Threshold_SumOfEntropies_HE_Nuclei,Image|Threshold_SumOfEntropies_Tissue,Image|Threshold_FinalThreshold_HE_Nuclei,Image|Threshold_FinalThreshold_Tissue,Image|Threshold_WeightedVariance_Tissue,Image|Threshold_WeightedVariance_HE_Nuclei,Image|Threshold_OrigThreshold_HE_Nuclei,Image|Threshold_OrigThreshold_Tissue,Image|PathName_Original,Image|Group_Index,Image|Group_Number,Image|Count_Tissue,Image|Count_HE_Nuclei,Image|ModuleError_10IdentifyPrimaryObjects,Image|ModuleError_03NamesAndTypes,Image|ModuleError_09UnmixColors,Image|ModuleError_07ImageMath,Image|ModuleError_06Smooth,Image|ModuleError_08IdentifyPrimaryObjects,Image|ModuleError_12SaveImages,Image|ModuleError_11OverlayOutlines,Image|ModuleError_05ColorToGray,Image|ModuleError_13MeasureImageAreaOccupied,Image|ModuleError_01Images,Image|ModuleError_02Metadata,Image|ModuleError_04Groups,Image|ExecutionTime_10IdentifyPrimaryObjects,Image|ExecutionTime_02Metadata,Image|ExecutionTime_06Smooth,Image|ExecutionTime_12SaveImages,Image|ExecutionTime_03NamesAndTypes,Image|ExecutionTime_11OverlayOutlines,Image|ExecutionTime_05ColorToGray,Image|ExecutionTime_01Images,Image|ExecutionTime_09UnmixColors,Image|ExecutionTime_13MeasureImageAreaOccupied,Image|ExecutionTime_07ImageMath,Image|ExecutionTime_08IdentifyPrimaryObjects,Image|ExecutionTime_04Groups,Image|Series_Original,Image|Height_Original,Image|URL_Original,Image|Scaling_Original,Image|FileName_Original,Experiment|CellProfiler_Version,Experiment|Modification_Timestamp,Experiment|Pipeline_Pipeline,Experiment|Run_Timestamp,Tissue|Location_Center_X,Tissue|Location_Center_Y,Tissue|Location_Center_Z,Tissue|Number_Object_Number,HE_Nuclei|Location_Center_Y,HE_Nuclei|Location_Center_X,HE_Nuclei|Location_Center_Z,HE_Nuclei|Number_Object_Number + Representation of Nan/Inf:NaN + Add a prefix to file names?:Yes + Filename prefix:HE_ + Overwrite existing files without warning?:Yes + Data to export:Do not use + Combine these object measurements with those of the previous object?:No + File name:HE.csv + Use the object name for the file name?:Yes +" +Run_Timestamp,2021-11-20T23:48:54.287444 diff --git a/upgrade_study/HE/summary/cp_output/HE_HE_Nuclei.csv b/upgrade_study/HE/summary/cp_output/HE_HE_Nuclei.csv new file mode 100644 index 0000000..f41ebbe --- /dev/null +++ b/upgrade_study/HE/summary/cp_output/HE_HE_Nuclei.csv @@ -0,0 +1,568 @@ +ImageNumber,ObjectNumber,Metadata_Date,Metadata_FileLocation,Metadata_Frame,Metadata_NR,Metadata_STAIN,Metadata_Series,Metadata_T_NUMBER,Metadata_X,Metadata_Y,Location_Center_X,Location_Center_Y,Location_Center_Z,Number_Object_Number 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diff --git a/upgrade_study/HE/summary/cp_output/HE_Image.gct b/upgrade_study/HE/summary/cp_output/HE_Image.gct new file mode 100644 index 0000000..1f0c694 --- /dev/null +++ b/upgrade_study/HE/summary/cp_output/HE_Image.gct @@ -0,0 +1,6 @@ +#1.2 +3 48 +NAME Description AreaOccupied_AreaOccupied_HE_Nuclei AreaOccupied_AreaOccupied_Tissue AreaOccupied_Perimeter_HE_Nuclei AreaOccupied_Perimeter_Tissue AreaOccupied_TotalArea_HE_Nuclei AreaOccupied_TotalArea_Tissue Channel_Original Count_HE_Nuclei Count_Tissue Frame_Original ImageSet_ImageSet Mean_HE_Nuclei_Location_Center_X Mean_HE_Nuclei_Location_Center_Y Mean_HE_Nuclei_Location_Center_Z Mean_Tissue_Location_Center_X Mean_Tissue_Location_Center_Y Mean_Tissue_Location_Center_Z Median_HE_Nuclei_Location_Center_X Median_HE_Nuclei_Location_Center_Y Median_HE_Nuclei_Location_Center_Z Median_Tissue_Location_Center_X Median_Tissue_Location_Center_Y Median_Tissue_Location_Center_Z Metadata_Date Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_STAIN Metadata_Series Metadata_T_NUMBER Metadata_X Metadata_Y ProcessingStatus Series_Original StDev_HE_Nuclei_Location_Center_X StDev_HE_Nuclei_Location_Center_Y StDev_HE_Nuclei_Location_Center_Z StDev_Tissue_Location_Center_X StDev_Tissue_Location_Center_Y StDev_Tissue_Location_Center_Z Threshold_FinalThreshold_HE_Nuclei Threshold_FinalThreshold_Tissue Threshold_OrigThreshold_HE_Nuclei Threshold_OrigThreshold_Tissue Threshold_SumOfEntropies_HE_Nuclei Threshold_SumOfEntropies_Tissue Threshold_WeightedVariance_HE_Nuclei Threshold_WeightedVariance_Tissue +AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png /Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow 32939.0 2279158.0 12541.0 7494.0 4000000.0 4000000.0 -2 169.0 1.0 0 120 1240.5198370944568 1302.7646216942157 0.0 1268.6499913564571 1293.7435500303181 0.0 1257.1896551724137 1346.6666666666667 0.0 1268.6499913564571 1293.7435500303181 0.0 nan file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png 0 AE1468 HE_x20_z0 0 UMC X1 Y5 Done 0 417.4931571676391 438.4431887238187 0.0 0.0 0.0 0.0 0.7 0.065 0.036763073980548344 0.065 -9.95687799547462 -11.241437822808415 9.93453277956218 0.26998894705474374 +AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png /Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow 80798.0 2433742.0 38904.0 7465.0 4000000.0 4000000.0 -2 397.0 2.0 0 120 1205.3385169801566 1134.669593706267 0.0 703.7525412870958 1481.7840084623529 0.0 1142.570945945946 1239.523404255319 0.0 703.7525412870958 1481.7840084623529 0.0 nan file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png 0 AE1468 HE_x20_z0 0 UMC X2 Y1 Done 0 359.68978901843604 581.5595758774974 0.0 671.960046097872 390.52067402642206 0.0 0.7 0.065 0.03923134747449358 0.065 -10.062639891414028 -11.32677594010671 9.60191077942776 0.2459807632845789 +AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png /Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow 117.0 3841620.0 51.0 8811.0 4000000.0 4000000.0 -2 1.0 1.0 0 120 811.7692307692307 453.47008547008545 0.0 985.0137790827828 1037.2354137056764 0.0 811.7692307692307 453.47008547008545 0.0 985.0137790827828 1037.2354137056764 0.0 nan file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png 0 AE1468 HE_x20_z0 0 UMC X3 Y8 Done 0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 0.065 0.030251547638800315 0.065 -9.537722405170964 -11.639320639896948 6.685294541391725 0.6070315215113758 diff --git a/upgrade_study/HE/summary/cp_output/HE_Tissue.csv b/upgrade_study/HE/summary/cp_output/HE_Tissue.csv new file mode 100644 index 0000000..496579b --- /dev/null +++ b/upgrade_study/HE/summary/cp_output/HE_Tissue.csv @@ -0,0 +1,5 @@ +ImageNumber,ObjectNumber,Metadata_Date,Metadata_FileLocation,Metadata_Frame,Metadata_NR,Metadata_STAIN,Metadata_Series,Metadata_T_NUMBER,Metadata_X,Metadata_Y,Location_Center_X,Location_Center_Y,Location_Center_Z,Number_Object_Number +1,1,nan,file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X1.Y5.normalized.tile.tissue.png,0,AE1468,HE_x20_z0,0,UMC,X1,Y5,1268.6499913564571,1293.7435500303181,0,1 +2,1,nan,file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png,0,AE1468,HE_x20_z0,0,UMC,X2,Y1,1375.7125873849677,1091.2633344359308,0,1 +2,2,nan,file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X2.Y1.normalized.tile.tissue.png,0,AE1468,HE_x20_z0,0,UMC,X2,Y1,31.79249518922386,1872.304682488775,0,2 +3,1,nan,file:/Users/slaan3/git/swvanderlaan/slideToolKit/upgrade_study/HE/summary/AE1468.UMC.HE_x20_z0.tiles_workflow/AE1468.UMC.HE_x20_z0.X3.Y8.normalized.tile.tissue.png,0,AE1468,HE_x20_z0,0,UMC,X3,Y8,985.0137790827828,1037.2354137056764,0,1 diff --git a/utilities/Colsums_CD3_CD56_NKT.R b/utilities/Colsums_CD3_CD56_NKT.R new file mode 100755 index 0000000..02c82ee --- /dev/null +++ b/utilities/Colsums_CD3_CD56_NKT.R @@ -0,0 +1,43 @@ +# cat("+++++ Collecting data from analysis +++++\n") +# cleanup +rm(list = ls()) + +# cat("Working directory: ") +# getwd() + +# cat("\n..... loading required libraries\n") +# load the required libraries +library(data.table) +library(DT) + +# cat("\n..... parsing arguments\n") +# parse arguments +args = commandArgs(trailingOnly=TRUE) +# debug +# args + +# cat("\n..... reading data\n") +# read the data +df = fread(input = paste0(args[1], "_", args[2]), + verbose = FALSE, showProgress = TRUE) + +# for debug +#df[1:10,] +# str(df) +# dim(df) + +# cat("\n..... parsing data\n") +# parse the data +# specific for a stain +VAL = sum(df$AreaOccupied_AreaOccupied_NKT_OBJ) / sum(df$AreaOccupied_TotalArea_NKT_OBJ) +# original code +# VAL = sum(df$AreaOccupied_AreaOccupied_DAB_object_yellow) / sum(df$AreaOccupied_AreaOccupied_Tissue_object_green) +if (is.na(VAL)) {VAL=0} +cat( VAL ) + +# cat("\nSession information\n") +# print(version) +# print(sessionInfo()) +# cat(paste0("Library path(s) [ ", .libPaths()), "]\n") + +# cat("\nCopyright (C) 1979-2021 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | https://swvanderlaan.github.io.") diff --git a/utilities/Colsums_GLYCC.R b/utilities/Colsums_GLYCC.R new file mode 100644 index 0000000..9708d32 --- /dev/null +++ b/utilities/Colsums_GLYCC.R @@ -0,0 +1,43 @@ +# cat("+++++ Collecting data from analysis +++++\n") +# cleanup +rm(list = ls()) + +# cat("Working directory: ") +# getwd() + +# cat("\n..... loading required libraries\n") +# load the required libraries +library(data.table) +library(DT) + +# cat("\n..... parsing arguments\n") +# parse arguments +args = commandArgs(trailingOnly=TRUE) +# debug +# args + +# cat("\n..... reading data\n") +# read the data +df = fread(input = paste0(args[1], "_", args[2]), + verbose = TRUE, showProgress = TRUE) + +# for debug +#df[1:10,] +# str(df) +# dim(df) + +# cat("\n..... parsing data\n") +# parse the data +# specific for a stain +# VAL = sum(df$AreaOccupied_AreaOccupied_NKT_OBJ) / sum(df$AreaOccupied_TotalArea_NKT_OBJ) +# original code +VAL = sum(df$AreaOccupied_AreaOccupied_DAB_object) / sum(df$AreaOccupied_AreaOccupied_Tissue_object) +if (is.na(VAL)) {VAL=0} +cat( VAL ) + +# cat("\nSession information\n") +# print(version) +# print(sessionInfo()) +# cat(paste0("Library path(s) [ ", .libPaths()), "]\n") + +# cat("\nCopyright (C) 1979-2021 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | https://swvanderlaan.github.io.") diff --git a/utilities/Colsums_Generic.R b/utilities/Colsums_Generic.R new file mode 100755 index 0000000..6030b5f --- /dev/null +++ b/utilities/Colsums_Generic.R @@ -0,0 +1,43 @@ +# cat("+++++ Collecting data from analysis +++++\n") +# cleanup +rm(list = ls()) + +# cat("Working directory: ") +# getwd() + +# cat("\n..... loading required libraries\n") +# load the required libraries +library(data.table) +library(DT) + +# cat("\n..... parsing arguments\n") +# parse arguments +args = commandArgs(trailingOnly=TRUE) +# debug +# args + +# cat("\n..... reading data\n") +# read the data +df = fread(input = paste0(args[1], "_", args[2]), + verbose = FALSE, showProgress = TRUE) + +# for debug +#df[1:10,] +# str(df) +# dim(df) + +# cat("\n..... parsing data\n") +# parse the data +# specific for a stain +# VAL = sum(df$AreaOccupied_AreaOccupied_NKT_OBJ) / sum(df$AreaOccupied_TotalArea_NKT_OBJ) +# original code +VAL = sum(df$AreaOccupied_AreaOccupied_DAB_object_yellow) / sum(df$AreaOccupied_AreaOccupied_Tissue_object_green) +if (is.na(VAL)) {VAL=0} +cat( VAL ) + +# cat("\nSession information\n") +# print(version) +# print(sessionInfo()) +# cat(paste0("Library path(s) [ ", .libPaths()), "]\n") + +# cat("\nCopyright (C) 1979-2021 Sander W. van der Laan | s.w.vanderlaan[at]gmail.com | https://swvanderlaan.github.io.") diff --git a/utilities/CreateAssessment.py b/utilities/CreateAssessment.py new file mode 100755 index 0000000..9b28ef3 --- /dev/null +++ b/utilities/CreateAssessment.py @@ -0,0 +1,44 @@ +import os +import numpy as np +import pandas as pd +import glob +from functools import reduce + +stainlist = ["CD68", "CD34", "EVG", "SMA", "HE", "SR", "FIBRIN", "SR_POLARIZED", "CD66b","GLYCC"] +stainlisthpc = ["CD68_2", "CD34", "EVG", "SMA", "HE", "FIBRIN", "SR_POLARIZED","SR","ndpiSR"] +pathbasebulk = "/data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/" +pathbasehpc = "/hpc/dhl_ec/VirtualSlides/" +dflistbulk = [] +dflisthpc = [] + +for i in stainlist: + pathlist = [] + aelist = [] + for directory, child, output in os.walk(pathbasebulk+i): + if len(output) > 1 and "OTHER" not in directory and "RENAME" not in directory and "OKAY" not in directory: + for file in output: + if ".TIF" in file or ".ndpi" in file: + pathlist.append(directory+"/"+file) + aelist.append(file[file.index("AE"):file.index(".")]) + if len(output) == 1: + if ".TIF" in output or ".ndpi" in output: + pathlist.append(directory+"/"+output[0]) + aelist.append(output[output.index("AE"):output.index(".")]) + dflistbulk.append(pd.DataFrame({'AE':aelist,'FILEPATH_'+i:pathlist})) +bulkdf = reduce(lambda x, y: pd.merge(x,y,on='AE',how='outer'),dflistbulk) + +for i in stainlisthpc: + reslist = [] + aelist = [] + for directory,child,output in os.walk(pathbasehpc+i): + if output: + if "txt" in output[0] or "csv" in output[0]: + if "cp_output" in directory and "AE" in directory: + reslist.append("yes") + aelist.append(directory[directory.index("AE"):directory.index(".")]) + dflisthpc.append(pd.DataFrame({'AE':aelist,'RESULTCHECK_'+i:reslist})) +hpcdf = reduce(lambda x, y: pd.merge(x,y,on='AE',how='outer'),dflisthpc) + +full_report = pd.merge(bulkdf,hpcdf,on='AE',how='outer') +full_report.drop_duplicates(inplace=True) +full_report.to_csv(path_or_buf="assessment.csv",sep=";",index=False) diff --git a/utilities/checkoutput.sh b/utilities/checkoutput.sh new file mode 100755 index 0000000..6d6ea6c --- /dev/null +++ b/utilities/checkoutput.sh @@ -0,0 +1,43 @@ +# ORIGINAL CODE +# find AE* -maxdepth 0 | while read line; do +# if [ $(ls "$line"/cp_output/CD* | wc -l) -eq 7 ]; then +# echo $line +# rm "$line"/*.TIF +# completed=$((completed+1)) +# echo $completed +# else +# rm -v -rf "$line"/cp_output +# rm -v -rf "$line"/*tiles* +# rm -v "$line"/*.png +# rm -v "$line"/result.txt +# rm -v "$line"/processanda.txt +# rm -v -rf "$line"/magick* +# fi +# done +# +# echo "total plaques" +# find AE* -maxdepth 0 | wc -l + + +# UPDATED code + +find AE* -maxdepth 0 | while read line; do + if find "$line"/cp_output/cp_output -mindepth 1 | read; then + echo $line + ls "$line"/cp_output/cp_output + rm "$line"/*.tif + else + rm -v -rf "$line"/cp_output + rm -v -rf "$line"/*tiles* + rm -v "$line"/*.png + rm -v "$line"/result.txt + rm -v "$line"/processanda.txt + rm -v -rf "$line"/magick* + fi +done +echo "total plaques" +find AE* -maxdepth 0 | wc -l +echo completed plaques +ls AE*/cp_output/cp_output/Output_Image.csv | wc -l + + diff --git a/utilities/crop.py b/utilities/crop.py new file mode 100755 index 0000000..dba3eb9 --- /dev/null +++ b/utilities/crop.py @@ -0,0 +1,20 @@ +import cv2 +from sys import argv + +top = float(argv[1]) +bottom = float(argv[2]) +left = float(argv[3]) +right = float(argv[4]) +path = argv[5] + +def crop_img(img): + center_x, center_y = img.shape[1] / 2, img.shape[0] / 2 + left_x, right_x = center_x - img.shape[1]*left / 2, center_x + img.shape[1]*right / 2 + top_y, bottom_y = center_y - img.shape[0]*top / 2, center_y + img.shape[0]*bottom / 2 + img_cropped = img[int(top_y):int(bottom_y), int(left_x):int(right_x)] + return img_cropped + + +img = cv2.imread(path) +img_cropped = crop_img(img) +cv2.imwrite("cropped_" + path,img_cropped) diff --git a/utilities/hash_files.py b/utilities/hash_files.py new file mode 100755 index 0000000..071ad3b --- /dev/null +++ b/utilities/hash_files.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python3 +# Reference: https://askubuntu.com/questions/61826/how-do-i-check-the-sha1-hash-of-a-file +import sys +import hashlib +import os +from collections import OrderedDict as od + +def get_hashsums(file_path): + hash_sums = od() +# hash_sums['md5sum'] = hashlib.md5() +# hash_sums['sha1sum'] = hashlib.sha1() +# hash_sums['sha224sum'] = hashlib.sha224() + hash_sums['sha256sum'] = hashlib.sha256() +# hash_sums['sha384sum'] = hashlib.sha384() +# hash_sums['sha512sum'] = hashlib.sha512() + + with open(file_path, 'rb') as fd: + data_chunk = fd.read(65536) + while data_chunk: + for hashsum in hash_sums.keys(): + hash_sums[hashsum].update(data_chunk) + data_chunk = fd.read(65536) + + results = od() + for key,value in hash_sums.items(): + results[key] = value.hexdigest() + return results + + + +def main(): + for path in sys.argv[1:]: + print(">>> ",path) + for key,value in get_hashsums(path).items(): + print(key,value) + +if __name__ == '__main__': main() \ No newline at end of file diff --git a/utilities/ndpi2tif.sh b/utilities/ndpi2tif.sh new file mode 100755 index 0000000..45dbc72 --- /dev/null +++ b/utilities/ndpi2tif.sh @@ -0,0 +1,120 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " NDPI2TIF" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim van de Kerkhof" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-07-18" +echoitalic "* Version : 1.0.1" +echo "" +echoitalic "* Description : This script will convert NDPI-files to TIF-format." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +### Make directories for script if they do not exist yet (!!!PREREQUISITE!!!) +if [ ! -d $(pwd)/errors/ ]; then + mkdir -v $(pwd)/errors/ +fi +if [ ! -d $(pwd)/logs/ ]; then + mkdir -v $(pwd)/logs/ +fi +ERRORS=$(pwd)/errors +LOGS=$(pwd)/logs + +find $(pwd)/*.ndpi | while read line; do + echo "" + echocyan "processing file [ $line ]" + FILENAME=$(basename "$line" .ndpi) + echo " + module load ndpitools + ndpisplit -v -x20 -z0 '$line' + rm '$line'" | qsub -cwd -e $ERRORS/ -o $LOGS/ -N $FILENAME +done + +script_copyright_message diff --git a/utilities/qualityCheck.py b/utilities/qualityCheck.py new file mode 100755 index 0000000..f886e53 --- /dev/null +++ b/utilities/qualityCheck.py @@ -0,0 +1,43 @@ +import glob +import os +import shutil +from sys import argv + + +bulkPathBaseAE = "/data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/" + +stainHPC = argv[1] +stainBulk = argv[2] +XNum = argv[3] +subDir = argv[4] +copy = argv[5] +filepath = (stainHPC+"/failed_slides/AEprocesslist.txt") + +checkList = [] + +for i in glob.glob(stainHPC + "/" + XNum + "*/" + subDir): + if not os.listdir(i): + checkList.append(i) + +print("total number of unprocessed slides: " + str(len(checkList))) + +if not os.path.exists(stainHPC + "/failed_slides"): + os.makedirs(stainHPC + "/failed_slides") + +copyList = [] +if copy == "y": + if XNum == "AE": + print("Using AE") + for slide in checkList: + slide = slide.split("/")[-2] + if not glob.glob(stainHPC + "/failed_slides/" + slide + "*"): + copyList.append(glob.glob(bulkPathBaseAE + stainBulk + "/" + slide + "*")[0]) + print("now copying " + str(len(copyList)) + " slides that remain from the total") + + if copyList: + with open(filepath, 'w') as f: + for origfile in copyList: + print("now copying " + origfile + " to failed slides file") + + f.write("%s\n" % origfile) + diff --git a/utilities/removeDuplicates.py b/utilities/removeDuplicates.py new file mode 100755 index 0000000..88ecc28 --- /dev/null +++ b/utilities/removeDuplicates.py @@ -0,0 +1,173 @@ +#!/usr/bin/env python3 +# if running in py3, change the shebang, drop the next import for readability (it does no harm in py3) +# from __future__ import print_function # py2 compatibility + +# Reference: https://stackoverflow.com/a/36113168/4322048 + +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print(" slideDuplicates: identify duplicate slides ") +print("") +print("* Version : v1.0.0") +print("") +print("* Last update : 2021-11-18") +print("* Written by : Sander W. van der Laan | s.w.vanderlaan@gmail.com") +print("* Inspired by : Todor Minakov") +print("") +print("* Description : This script will check has by size, and content of files to identify duplicate images ") +print(" for inspection and (manual) removal.") +print("") +print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + +# fix argument parser +# add in option check same study number, choose the most recent one, +# make sure it's size is larger then 1Mb, and move all others +# to a new directory "_duplicates" +# add in option to rename files to +# 1) include proper stain (requirement), +# 2) remove spaces, dashes, and (), +# only preserving files like AExxxx.[UMC/Txx-xxxxx].[STAIN].[ext] +# where [UMC/Txx-xxxxx] may not exist + + +# import required packages +from collections import defaultdict +import hashlib +import os +import sys + +# parser = argparse.ArgumentParser( +# prog='slideDuplicates', +# description='This script will check has by size, and content of files to identify duplicate images for inspection and (manual) removal.', +# usage='slideDuplicates -i/--input -v/--verbose; for help: -h/--help', +# formatter_class=argparse.RawDescriptionHelpFormatter, +# epilog=textwrap.dedent("Copyright (c) 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl")) +# +# parser.add_argument('-s', '--studynr', help="Check files on studynumbers, i.e. AExxxx or AAAxxxx.", default="", type=str) +# parser.add_argument('-r', '--rename', help="Rename files to the form AExxxx.[UMC/Txx-xxxxx].[STAIN].[ext].", default="", type=str) +# parser.add_argument('-v', '--verbose', help="While writing images also display image properties.", default=False, action="store_true") +# +# requiredNamed = parser.add_argument_group('required named arguments') +# +# requiredNamed.add_argument('-i','--input',help="Input (directory containing files). Try: *.TIF or /path_to/images/*.ndpi.", nargs="*") +# +# args = parser.parse_args() +# +# if not args.input: +# print("\nOh, computer says no! You must supply correct arguments when running a *** slideDuplicates ***!") +# print("Note that -i/--input is required. Try: *.TIF or /path_to/images/*.ndpi.\n") +# parser.print_help() +# exit() +# +# if len(args.input) > 1: # bash has sent us a list of files +# files = args.input +# else: # user sent us a wildcard, need to use glob to find files +# files = glob.glob(args.input[0]) + +def chunk_reader(fobj, chunk_size=1024): + """Generator that reads a file in chunks of bytes""" + while True: + chunk = fobj.read(chunk_size) + if not chunk: + return + yield chunk + + +def get_hash(filename, first_chunk_only=False, hash=hashlib.sha1): + hashobj = hash() + file_object = open(filename, 'rb') + + if first_chunk_only: + hashobj.update(file_object.read(1024)) + else: + for chunk in chunk_reader(file_object): + hashobj.update(chunk) + hashed = hashobj.digest() + + file_object.close() + return hashed + + +def check_for_duplicates(paths, hash=hashlib.sha1): + hashes_by_size = defaultdict(list) # dict of size_in_bytes: [full_path_to_file1, full_path_to_file2, ] + hashes_on_1k = defaultdict(list) # dict of (hash1k, size_in_bytes): [full_path_to_file1, full_path_to_file2, ] + hashes_full = {} # dict of full_file_hash: full_path_to_file_string + + for path in paths: + for dirpath, dirnames, filenames in os.walk(path): + # get all files that have the same size - they are the collision candidates + for filename in filenames: + full_path = os.path.join(dirpath, filename) + try: + # if the target is a symlink (soft one), this will + # dereference it - change the value to the actual target file + full_path = os.path.realpath(full_path) + file_size = os.path.getsize(full_path) + hashes_by_size[file_size].append(full_path) + except (OSError,): + # not accessible (permissions, etc) - pass on + continue + + + # For all files with the same file size, get their hash on the 1st 1024 bytes only + for size_in_bytes, files in hashes_by_size.items(): + if len(files) < 2: + continue # this file size is unique, no need to spend CPU cycles on it + + for filename in files: + try: + small_hash = get_hash(filename, first_chunk_only=True) + # the key is the hash on the first 1024 bytes plus the size - to + # avoid collisions on equal hashes in the first part of the file + # credits to @Futal for the optimization + hashes_on_1k[(small_hash, size_in_bytes)].append(filename) + except (OSError,): + # the file access might've changed till the exec point got here + continue + + # For all files with the hash on the 1st 1024 bytes, get their hash on the full file - collisions will be duplicates + for __, files_list in hashes_on_1k.items(): + if len(files_list) < 2: + continue # this hash of fist 1k file bytes is unique, no need to spend cpy cycles on it + + for filename in files_list: + try: + full_hash = get_hash(filename, first_chunk_only=False) + duplicate = hashes_full.get(full_hash) + if duplicate: + print("Duplicates found (same size and content): {} and {}".format(filename, duplicate)) + else: + hashes_full[full_hash] = filename + except (OSError,): + # the file access might've changed till the exec point got here + continue + + +if __name__ == "__main__": + if sys.argv[1:]: + check_for_duplicates(sys.argv[1:]) + else: + print("Please pass the paths to check as parameters to the script.") + + +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") +print("+ The MIT License (MIT) +") +print("+ Copyright (c) 1979-2021 Sander W. van der Laan | UMC Utrecht, Utrecht, the Netherlands +") +print("+ +") +print("+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +") +print("+ associated documentation files (the \"Software\"), to deal in the Software without restriction, including +") +print("+ without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +") +print("+ copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the +") +print("+ following conditions: +") +print("+ +") +print("+ The above copyright notice and this permission notice shall be included in all copies or substantial +") +print("+ portions of the Software. +") +print("+ +") +print("+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT +") +print("+ LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO +") +print("+ EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER +") +print("+ IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR +") +print("+ THE USE OR OTHER DEALINGS IN THE SOFTWARE. +") +print("+ +") +print("+ Reference: http://opensource.org. +") +print("+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") + diff --git a/utilities/rename.files.sh b/utilities/rename.files.sh new file mode 100755 index 0000000..25d6c7c --- /dev/null +++ b/utilities/rename.files.sh @@ -0,0 +1,176 @@ +#!/bin/bash +# +# It is good practice to properly name and annotate your script for future reference for +# yourself and others. Trust me, you'll forget why and how you made this!!! + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the recode file which contains two columns (old and new name) WITHOUT header." + echoerror "- Argument #2 -- (the path to) the images" + echoerror "" + echoerror "An example command would be: rename.files.sh [arg1: rename.files.txt] [arg2: some/dir/here] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echobold "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " rename.files" +echobold "" +echobold "* Written by : Sander W. van der Laan" +echobold "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echobold "* Last update : 2021-11-16" +echobold "* Version : 1.2" +echobold "" +echobold "* Description : This script will rename files based on a two arguments and a." +echobold " file containing the old and new names." +echobold "" +echobold "* Example input: ./rename.files.sh recode.txt inputdir" +echobold "* Example [ recode.txt ]" +echobold "AE1149.T06-25428_A.SMA.20141016.ndpi AE1149.T06-25428.A.SMA.20141016.ndpi" +echobold "AE1419.T06-24242_A.SMA.20141016.ndpi AE1419.T06-24242.A.SMA.20141016.ndpi" +echobold "AE1556.T07-13869_A.SMA.20141016.ndpi AE1556.T07-13869.A.SMA.20141016.ndpi" +echobold "" +echobold "Today's: "$(date) +TODAY=$(date +"%Y%m%d") +echobold "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "" +echo "------------------------------------------------------------------------------------" +echobold "Checking input." + +echo "" + +if [[ $# -lt 2 ]]; then + echoerrorflash "Oh, computer says no! Number of arguments found $#." + script_arguments_error "You must supply correct (number of) arguments when running *** rename.files ***!" + +else + + if ! [ -f "$1" ];then + echoerrorflash "No valid recode file found." + echoerrorflash "Usage "$(basename "$0")" recodefile.txt /input/dir/" + exit 1 + fi + if ! [ -d "$2" ];then + echoerrorflash "No valid directory found." + echoerrorflash "Usage "$(basename "$0")" recodefile.txt /input/dir/" + exit 1 + fi + + echo "" + echo "------------------------------------------------------------------------------------" + echobold "Input was correct. Preparing move." + while IFS='' read -r FILENAMES || [[ -n "$FILENAMES" ]]; do + LINE=${FILENAMES} + + OLDNAME=$(echo "${LINE}" | awk 'BEGIN{FS="\t"} {print $1}' ) + NEWNAME=$(echo "${LINE}" | awk 'BEGIN{FS="\t"} {print $2}' ) + echo "" + echo "The old filename is: [ $(ls "${OLDNAME}") ]" + echo "The new filename is: [ $NEWNAME ]" + + if [ ! -z "${OLDNAME}" ]; then # if non-zero + echoerrorflash "The old filename is not present. " + + if [ ! -z "$NEWNAME" ]; then # if non-zero + + echo "Moving files..." + mv -nv "$2/${OLDNAME}" "$2/$NEWNAME" # move the old name to the new (second) name" + + fi + fi + done < "$1" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message + +echo "" +echobold "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold "Wow. I'm all done buddy. What a job! let's have đŸģ and 🖖 !" +date diff --git a/utilities/slideAppend.sh b/utilities/slideAppend.sh new file mode 100755 index 0000000..a949580 --- /dev/null +++ b/utilities/slideAppend.sh @@ -0,0 +1,224 @@ +#!/bin/bash +# +# It is good practice to properly name and annotate your script for future reference for +# yourself and others. Trust me, you'll forget why and how you made this!!! + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- study type of the analysis, e.g. AE or AAA." + echoerror "- Argument #3 -- image file type, e.g. TIF, NDPI." + echoerror "- Argument #4 -- (the path to) the filename, e.g. Image.csv.gz, the script will automatically append STAIN, e.g. STAIN_Image.csv.gz" + echoerror "" + echoerror "An example command would be: slideAppend [arg1: STAIN] [arg2: STUDYTYPE ] [arg3: IMAGETYPE ] [arg4: Output_Image.csv.gz] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideAppend" +echo "" +echoitalic "* Written by : Tim G.M. van de Kerkhof; Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-26" +echoitalic "* Version : v1.0.4" +echo "" +echoitalic "* Description : This script will collect results and append these in a CSV." +echoitalic " Input CSV-files are expected to be gzipped." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "Today's: $(date)" +TODAY=$(date +"%Y%m%d") # set Today + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +STUDYTYPE="$2" +IMAGETYPE="$3" +RESULTSFILENAME="$4" + +if [[ $# -lt 4 ]]; then + echoerrorflash "Oh, computer says no! Number of arguments found $#." + script_arguments_error "You must supply correct (number of) arguments when running *** slideAppend ***!" + +else + + OutFileName="${TODAY}.${STAIN}.${STUDYTYPE}.${IMAGETYPE}.ImageExp.csv" # Fix the output name + + echo "" + echoitalic "Creating a new log." + # make a new append.log + echo "STUDYNUMBER FILENAME" > "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".append.log + + echo "" + echoitalic "Creating folders to collect all masks and tile-crossed images." + mkdir -v "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".MasksTileCrossedImages + MASKDIR="${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".MasksTileCrossedImages + mkdir -v $MASKDIR/_issues + MASKISSUEDIR="$MASKDIR/_issues" + + i=0 # Reset a counter + echo "" + echoitalic "Collecting data for:" + for filename in "$STUDYTYPE"*/cp_output/"${STAIN}"_"${RESULTSFILENAME}"; do + if [ "$filename" != "$OutFileName" ] ; then # Avoid recursion + # get the sample number from the filename + STUDYNUMBER=${filename%%\/*} # Remove everything from the first slash >> https://unix.stackexchange.com/questions/268134/extract-a-specific-part-of-the-path-of-a-file + + cols=$(zcat "$filename" | awk -F, '{ print NF }' | uniq | wc -l) + if [[ "$cols" -gt 1 ]]; then + STUDYNUMBER=${filename%%\/*} # Remove everything from the first slash >> https://unix.stackexchange.com/questions/268134/extract-a-specific-part-of-the-path-of-a-file + echo "**ERROR** no new line for end-of-file for [ $STUDYNUMBER ]." + echo "$STUDYNUMBER $filename" >> "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".append.log + cp -v $STUDYNUMBER/mask_${STUDYNUMBER}.png $MASKISSUEDIR/mask_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tilecrossed_${STUDYNUMBER}.png $MASKISSUEDIR/tilecrossed_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tile_selection.tsv $MASKISSUEDIR/tile_selection_${STUDYNUMBER}.tsv + + else + if [[ $i -eq 0 ]] ; then + echoitalic " First file no. $i: [ ${filename} ] ..." + ### DEBUG + ### echo "DEBUG: check head first file" + ### zcat "$filename" | head -1 + zcat "$filename" > "$OutFileName" # Copy header if it is the first file + cp -v $STUDYNUMBER/mask_${STUDYNUMBER}.png $MASKDIR/mask_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tilecrossed_${STUDYNUMBER}.png $MASKDIR/tilecrossed_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tile_selection.tsv $MASKDIR/tile_selection_${STUDYNUMBER}.tsv + + else + echoitalic " File no. $i: [ ${filename} ] ..." + ### DEBUG + ### echo "DEBUG: check head next file" + ### zcat "$filename" | head -2 + zcat "$filename" | tail -n +2 >> "$OutFileName" # Append from the 2nd line each file + + ### DEBUG + ### ls -lh "$OutFileName" + + cp -v $STUDYNUMBER/mask_${STUDYNUMBER}.png $MASKDIR/mask_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tilecrossed_${STUDYNUMBER}.png $MASKDIR/tilecrossed_${STUDYNUMBER}.png + cp -v $STUDYNUMBER/tile_selection.tsv $MASKDIR/tile_selection_${STUDYNUMBER}.tsv + + fi + fi + fi + i=$(( i + 1 )) # Increase the counter + + + done + + echo "" + echoitalic "Gzipping the shizzle." + gzip -vf "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".ImageExp.csv + + tar -zcvf "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".tar.gz "${MASKDIR}"/ + mv -v readme.slidemask.* "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".readme.slidemask.txt + mv -v readme.slide2tiles.* "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".readme.slide2tiles.txt + mv -v readme.slidenormalize.* "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".readme.slidenormalize.txt + mv -v readme.slidecelprofiler.* "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".readme.slidecelprofiler.txt + mv -v readme.slidewrapup.* "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".readme.slidewrapup.txt + + echo "" + echoitalic "Checking the log -- note: no results is a good thing." + cat "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".append.log + + echoitalic "==== NOTE ====" + echoitalic "You should lways check the log and review the tile-crossed images. " + echoitalic "The logs (summarized in 'readme'-files) might contain information " + echoitalic "regarding issues with slides run." + echoitalic "The tile-crossed images may involve samples that have too little" + echoitalic "or too many crosses due to debris, air bubbles, smudges, or other" + echoitalic "artefacts on the slides. Those samples you may want to re-run, re-" + echoitalic "scan, or exclude." + + echo "" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message diff --git a/utilities/slideAppendGCT.sh b/utilities/slideAppendGCT.sh new file mode 100755 index 0000000..aa58a2b --- /dev/null +++ b/utilities/slideAppendGCT.sh @@ -0,0 +1,200 @@ +#!/bin/bash +# +# It is good practice to properly name and annotate your script for future reference for +# yourself and others. Trust me, you'll forget why and how you made this!!! + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the stain as it appears in the filenames, e.g. FIBRIN." + echoerror "- Argument #2 -- study type of the analysis, e.g. AE or AAA." + echoerror "- Argument #3 -- image file type, e.g. TIF, NDPI." + echoerror "- Argument #4 -- (the path to) the filename, e.g. Image.gct.gz, the script will automatically append STAIN, e.g. STAIN_Image.gct.gz" + echoerror "" + echoerror "An example command would be: slideAppendGCT [arg1: STAIN] [arg2: STUDYTYPE ] [arg3: IMAGETYPE ] [arg4: Output_Image.gct.gz] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideAppendGCT" +echo "" +echoitalic "* Written by : Tim G.M. van de Kerkhof; Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-26" +echoitalic "* Version : v1.0.0" +echo "" +echoitalic "* Description : This script will collect results and append these in a CSV." +echoitalic " Input CSV-files are expected to be gzipped." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echo "Today's: $(date)" +TODAY=$(date +"%Y%m%d") # set Today + +echo "" +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 +STUDYTYPE="$2" +IMAGETYPE="$3" +RESULTSFILENAME="$4" + +if [[ $# -lt 4 ]]; then + echoerrorflash "Oh, computer says no! Number of arguments found $#." + script_arguments_error "You must supply correct (number of) arguments when running *** slideAppendGCT ***!" + +else + + OutFileName="${TODAY}.${STAIN}.${STUDYTYPE}.${IMAGETYPE}.ImageExp.gct" # Fix the output name + + echo "" + echoitalic "Creating a new log." + # make a new append.log + echo "STUDYNUMBER FILENAME" > "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".appendgct.log + + i=0 # Reset a counter + echo "" + echoitalic "Collecting data for:" + for filename in "$STUDYTYPE"*/cp_output/"${STAIN}"_"${RESULTSFILENAME}"; do + if [ "$filename" != "$OutFileName" ] ; then # Avoid recursion + + cols=$(zcat "$filename" | tail -n +3 | awk -F'\t' '{ print NF }' | uniq | wc -l) + if [[ "$cols" -gt 1 ]]; then + STUDYNUMBER=${filename%%\/*} # Remove everything from the first slash >> https://unix.stackexchange.com/questions/268134/extract-a-specific-part-of-the-path-of-a-file + echo "**ERROR** no new line for end-of-file for [ $STUDYNUMBER ]." + echo "$STUDYNUMBER $filename" >> "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".appendgct.log + + else + if [[ $i -eq 0 ]] ; then + echoitalic " First file no. $i: [ ${filename} ] ..." + ### DEBUG + ### echo "DEBUG: check head first file" + ### zcat "$filename" | tail -n +3 | head -1 + zcat "$filename" | tail -n +3 > "$OutFileName" # Copy header if it is the first file + + else + echoitalic " File no. $i: [ ${filename} ] ..." + ### DEBUG + ### echo "DEBUG: check head next file" + ### zcat "$filename" | tail -n +4 | head -1 + zcat "$filename" | tail -n +4 >> "$OutFileName" # Append from the 2nd line each file + + ### DEBUG + ### ls -lh "$OutFileName" + + fi + fi + fi + i=$(( i + 1 )) # Increase the counter + done + + echo "" + echoitalic "Gzipping the shizzle." + gzip -vf "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".ImageExp.gct + ### DEBUG + ### echo "DEBUG: check heads and tails of concatenated output file" + ### zcat "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".ImageExp.gct.gz | head -3 + ### zcat "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".ImageExp.gct.gz | tail + + echo "" + echoitalic "Checking the log -- note: no results is a good thing." + cat "${TODAY}"."${STAIN}"."${STUDYTYPE}"."${IMAGETYPE}".append.log + + echoitalic "==== NOTE ====" + echoitalic "You should lways check the log and review the tile-crossed images. " + echoitalic "The logs (summarized in 'readme'-files) might contain information " + echoitalic "regarding issues with slides run." + echoitalic "The tile-crossed images may involve samples that have too little" + echoitalic "or too many crosses due to debris, air bubbles, smudges, or other" + echoitalic "artefacts on the slides. Those samples you may want to re-run, re-" + echoitalic "scan, or exclude." + + echo "" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message diff --git a/utilities/slideCollector b/utilities/slideCollector new file mode 100755 index 0000000..a1d15aa --- /dev/null +++ b/utilities/slideCollector @@ -0,0 +1,207 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 -- name of the file containing images to collect, e.g. 20190721.ExpressScan.QC.FIBRIN.top20.largest.countobjects.txt, where the first column contains the samplenumber." + echoerror "- Argument #2 -- path_to working directory, i.e. where all the image-subdirectories are." + echoerror "- Argument #3 -- name of the stain." + echoerror "- Argument #4 -- starting letters/characters of the image-subdirectories, e.g. IMG or AE or AAA." + echoerror "" + echoerror "An example command would be: slideCollector [arg1: FILE] [arg2: IMAGEDIR] [arg3: STAIN] [arg3: SUBDIRNAME] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +script_arguments_error_imagetype() { + echoerror "$1" + echoerror "" + echoerror " *** ERROR *** ERROR --- $(basename "${0}") --- ERROR *** ERROR ***" + echoerror "" + echoerror " You must supply the correct argument:" + echoerror " * [TIF]/[NDPI] -- provide a valid imagetype." + echoerror "" + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideCollector" +echo "" +echoitalic "* Written by : Sander W. van der Laan; Tim Bezemer; Tim van de Kerkhof" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-07-25" +echoitalic "* Version : 1.0.0" +echo "" +echoitalic "* Description : This script will collect images based on an arbitrary list." +echoitalic " " +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS +FILENAME="$1" # Depends on arg1 +PROJECTDIR="$2" # Depends on arg3 +STAIN="$3" # Depends on arg4 +FILESTART="$4" # Depends on arg4 + +# Set slideToolKit DIRECTORY +SLIDETOOLKITDIR="/hpc/local/CentOS7/dhl_ec/software/slideToolKit" + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 4 ]]; then + echo "Oh, computer says no! Number of arguments found \"$#\"." + script_arguments_error "You must supply correct (number of) arguments when running *** slideCollector ***!" + +else + +echo "" +echobold "Reading [ ""$FILENAME"" ] and collecting relevant samples for ""$STAIN""." + +OUTPUTDIR=$(basename "$FILENAME" .txt) + +echo "" +echoitalic "Making the output directory [ ""$OUTPUTDIR"" ] and necessary subfolder(s)." +mkdir -pv "$OUTPUTDIR" +mkdir -pv "$OUTPUTDIR"/counted +COUNTDIR="$OUTPUTDIR"/counted + +while IFS='' read -r SAMPLEOFINTEREST || [[ -n "$SAMPLEOFINTEREST" ]]; do + ### EXAMPLE VARIANT LIST + ### StudyNumber ImageType TotalSTAINTissue TotalTissue TotalSTAINTissueCount TotalTissueCounts PercentageSTAIN RatioSTAIN CountObjectsSTAIN + ### 1110 TIF 75776173.5344423 134013790.848719 15398583 7078 56.5435639530429 0.565435639530429 2175.55566544222 + ### 1140 TIF 50992299.447722 81524147.3481138 23205297 6201 62.5487062501633 0.625487062501633 3742.1862602806 + + LINE=${SAMPLEOFINTEREST} + SAMPLENUMBER=$(echo "${LINE}" | awk '{ print $1 }') + IMAGETYPE=$(echo "${LINE}" | awk '{ print $2 }') + + echo "$SAMPLENUMBER $IMAGETYPE" + if [[ ${IMAGETYPE} = "TIF" ]]; then + echo "" + echo "Processing imagetype [" "$IMAGETYPE" "]." + + echo "" + echoitalic "Copying macro and masks." + cp -v "$PROJECTDIR"/"${FILESTART}""${SAMPLENUMBER}"*/*.png "$OUTPUTDIR"/ + + echo "" + echoitalic "Copying macro and masks." + cp -v "$PROJECTDIR"/"${FILESTART}""${SAMPLENUMBER}"*/cp_output/*.png "$COUNTDIR"/ + + elif [[ ${IMAGETYPE} = "NDPI" ]]; then + + echo "" + echo "Processing imagetype [" "$IMAGETYPE" "]." + + echo "" + echoitalic "Copying macro and masks." + cp -v "$PROJECTDIR"/_ndpi/"${FILESTART}""${SAMPLENUMBER}"*/*.png "$OUTPUTDIR"/ + + echo "" + echoitalic "Copying macro and masks." + cp -v "$PROJECTDIR"/_ndpi/"${FILESTART}""${SAMPLENUMBER}"*/cp_output/*.png "$COUNTDIR"/ + + else + ### If arguments are not met then this error message will be displayed + script_arguments_error_imagetype + fi + +done < <(tail -n "+3" "$FILENAME") + +echo "" +echoitalic "Gzipping collection for [ ""$STAIN""]." + +tar -zcvf "$OUTPUTDIR".tar.gz "$OUTPUTDIR"/ + +# rm -rv "$OUTPUTDIR" + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message \ No newline at end of file diff --git a/utilities/slideCopy.py b/utilities/slideCopy.py new file mode 100755 index 0000000..0866ff5 --- /dev/null +++ b/utilities/slideCopy.py @@ -0,0 +1,48 @@ +# imports +import os +import shutil +import argparse +import textwrap +import datetime +NOWTIME = datetime.datetime.now() +# print NOWTIME.year, NOWTIME.month, NOWTIME.day, NOWTIME.hour, NOWTIME.minute, NOWTIME.second + +# evaluate arguments +parser = argparse.ArgumentParser( + prog='slideCopy', + description='This script copies images of a given stain from the BULK in series starting from a given lower to an upper bound.', + usage='slideCopy [-h/--help] -l/--lower LOWER -u/--upper UPPER -s/--stain STAIN', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("\nCopyright (c) 2018-2019 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl; \nTim G.M. van de Kerkhof | t.g.m.vandekerkhof@umcutrecht.nl.")) +parser.add_argument('-l','--lower', help='Give the lower bound of the folder to copy, e.g. 1.', type=int, required=True) +parser.add_argument('-u','--upper', help='Give the upper bound of the folder to copy, e.g. 100.', type=int, required=True) +parser.add_argument('-s','--stain', help='Give the name of the stain, e.g. CD34.', required=True) + +try: + args = parser.parse_args() + print("We are copying files for stain [" + args.stain + "] from series [" + args.lower + "-" + args.upper + "].\n") + +except SystemExit: + print("\033[1;31m \nOh, computer says no! You must supply correct arguments when running a *** slideCopy ***!\n") + parser.print_help() + exit() + +# Set vars for batch +lower = args.lower +upper = args.upper +batch = True +STAIN = args.stain +basedir = "/data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/"+STAIN+"/_" + +# folder = argv[1] +# dest = argv[2] + +#if batch == False: +# for filename in os.listdir(folder): +# shutil.copy(folder+"/"+filename,dest) +if batch == True: + for i in range(lower, upper+1): + for filename in os.listdir(basedir+str(i)+"/"): + shutil.copy(basedir+str(i)+"/"+filename,"/hpc/dhl_ec/VirtualSlides/"+STAIN+"/") + print(filename) +# print(i) diff --git a/utilities/slideCopy.sh b/utilities/slideCopy.sh new file mode 100644 index 0000000..9c88fd4 --- /dev/null +++ b/utilities/slideCopy.sh @@ -0,0 +1,121 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +function echosucces { + echo -e "${YELLOW}${1}${NONE}" +} +function importantnote { + echo -e "${CYAN}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2016-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideCopy" +echo "" +echoitalic "* Written by : Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2021-11-16" +echoitalic "* Version : 1.0.0" +echo "" +echoitalic "* Description : This script will copy a hardcode list of files for a " +echoitalic " given project directory. " +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +### REQUIRED | GENERALS + + +FILES="" +BULK_STORAGE="/data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/HE" + +for f in $FILES; do + echo "processing [ $f ]" + cp -vn ${BULK_STORAGE}/${f} _extra + +done + +script_copyright_message \ No newline at end of file diff --git a/utilities/slideDir.sh b/utilities/slideDir.sh new file mode 100755 index 0000000..54a6732 --- /dev/null +++ b/utilities/slideDir.sh @@ -0,0 +1,80 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echocyan { #'echobold' is the function name + echo -e "${CYAN}${1}${NONE}" # this is whatever the function needs to execute. +} +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { #'echobold' is the function name + echo -e "\033[3m${1}\033[0m" # this is whatever the function needs to execute. +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " DIRECTORY CREATOR" +echo "" +echoitalic "* Written by : Tim G.M. van de Kerkhof; Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-07-02" +echoitalic "* Version : 1.0.2" +echo "" +echoitalic "* Description : This script will create directories using slideDirectory from " +echoitalic " slideToolKit." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +echocyan "Fix filenames and replace 'spaces' with '_'." +for f in *\ *; do + mv -v "$f" "${f// /_}" +done + +echo "" +echocyan "Creating directories for NDPI-files." +# old version macOS based, '-iname' goes iteratively through all the folders in the folder +# find "$(pwd)" -iname "*.ndpi" -exec /hpc/local/CentOS7/dhl_ec/software/slideToolKit/slideDirectory -f "{}" \; +find *.ndpi -exec $(command -v slideDirectory) -f "{}" \; + +echo "" +echocyan "Creating directories for TIF-files." +# old version macOS based, '-iname' goes iteratively through all the folders in the folder +# find "$(pwd)" -iname "*.TIF" -exec /hpc/local/CentOS7/dhl_ec/software/slideToolKit/slideDirectory -f "{}" \; +find *.TIF -exec $(command -v slideDirectory) -f "{}" \; + +echo "" +echocyan "Creating directories for tif-files." +# old version macOS based, '-iname' goes iteratively through all the folders in the folder +# find "$(pwd)" -iname "*.TIF" -exec /hpc/local/CentOS7/dhl_ec/software/slideToolKit/slideDirectory -f "{}" \; +find *.tif -exec $(command -v slideDirectory) -f "{}" \; + +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold "Wow. I'm all done buddy. What a job! let's have a beer!" +date diff --git a/utilities/slideProcessor.py b/utilities/slideProcessor.py new file mode 100755 index 0000000..dda7d6b --- /dev/null +++ b/utilities/slideProcessor.py @@ -0,0 +1,61 @@ +# imports +import glob +import shutil +import os +import argparse +import textwrap +import datetime +NOWTIME = datetime.datetime.now() +# print NOWTIME.year, NOWTIME.month, NOWTIME.day, NOWTIME.hour, NOWTIME.minute, NOWTIME.second + +# evaluate arguments +parser = argparse.ArgumentParser( + prog='slideProcessor', + description='This script processes all images of a given stain from the BULK and puts them in a sequentially numbered series of folders.', + usage='slideProcessor [-h/--help] -s/--stain STAIN -p/--path PATH', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("Copyright (c) 2018-2019 Sander W. van der Laan | s.w.vanderlaan-2@umcutrecht.nl; Tim G.M. van de Kerkhof | t.g.m.vandekerkhof@umcutrecht.nl.")) +parser.add_argument('-s','--stain', help='Give the name of the stain, e.g. CD34', required=True) +parser.add_argument('-p','--path', help='Give the full path to where the stain directory resides, e.g. /data/isi/d/dhl/ec/VirtualSlides/AE-SLIDES/', required=True) + +try: + args = parser.parse_args() + print("We are moving files for stain [" + args.stain + "] in [" + args.path + "].") + +except SystemExit: + print("\nOh, computer says no! You must supply correct arguments when running a *** slideProcessor ***!\n") + parser.print_help() + exit() + +# globals +STAIN = args.stain +PATHS = args.path + "/" + STAIN +BASE = PATHS + "/_" + +TIFnames = glob.glob(PATHS+"/*.TIF") +NDPInames = glob.glob(PATHS+"/*.ndpi") + +counter = 0 +basenum = 1 +for tif in TIFnames: + if counter == 10: + basenum += 1 + counter = 0 + if not os.path.exists(BASE+str(basenum)): + os.makedirs(BASE+str(basenum)) +# print(BASE+str(basenum)+"/"+tif) + filename = tif.rsplit('/',1) + shutil.move(tif, BASE+str(basenum)+"/"+str(filename[1])) + counter += 1 + + +for tif in NDPInames: + if counter == 10: + basenum += 1 + counter = 0 + if not os.path.exists(BASE+str(basenum)): + os.makedirs(BASE+str(basenum)) +# print(BASE+str(basenum)+"/"+tif) + filename = tif.rsplit('/',1) + shutil.move(tif, BASE+str(basenum)+"/"+str(filename[1])) + counter += 1 diff --git a/utilities/slideRemoveImages.sh b/utilities/slideRemoveImages.sh new file mode 100755 index 0000000..9f0f5b0 --- /dev/null +++ b/utilities/slideRemoveImages.sh @@ -0,0 +1,78 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echocyan { #'echobold' is the function name + echo -e "${CYAN}${1}${NONE}" # this is whatever the function needs to execute. +} +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { #'echobold' is the function name + echo -e "\033[3m${1}\033[0m" # this is whatever the function needs to execute. +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideRemoveImages" +echo "" +echoitalic "* Written by : Tim G.M. van de Kerkhof; Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-02-17" +echoitalic "* Version : 1.0.2" +echo "" +echoitalic "* Description : This script will remove TIFs, NDPIs, and PNG when we have" +echoitalic " no use of them anymore." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +echo "" +echocyan "Removing TIF-files." +rm -rvf AE*/*.TIF +rm -rvf AE*/*/*.TIF +rm -rvf AE*/*/*/*.TIF + +echo "" +echocyan "Removing NDPI-files." +rm -rvf AE*/*.ndpi +rm -rvf AE*/*/*.ndpi +rm -rvf AE*/*/*/*.ndpi + +echo "" +echocyan "Removing PNG-files." +rm -rvf AE*/*.png +rm -rvf AE*/*/*.png +rm -rvf AE*/*/*/*.png + +echo "" +echocyan "Removing magick-tmp-folders." +rm -rvf AE*/magick-tmp + +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold "Wow. I'm all done buddy. What a job! let's have a beer!" +date diff --git a/utilities/slideResultCollectorCD68.sh b/utilities/slideResultCollectorCD68.sh new file mode 100755 index 0000000..543c5b9 --- /dev/null +++ b/utilities/slideResultCollectorCD68.sh @@ -0,0 +1,211 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2015-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 indicates the stain used." + echoerror "" + echoerror "An example command would be: slideResultCollectorCD68.sh [arg1: STAIN] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideResultCollector for CD68" +echo "" +echoitalic "* Written by : Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-01-23" +echoitalic "* Version : 1.0.1" +echo "" +echoitalic "* Description : This script will collect data for a object quantification," +echoitalic " such as DAB based CD68." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found "$#"." + script_arguments_error "You must supply correct arguments when running a *** RESULT CONCATENATOR ***!" + +else + +echo "All arguments are passed and correct. We will collect data for stain: [ $STAIN ] ." + + echo "" + echocyan "Collecting data for DAB OBJECTS." + + echo "ImageNumber ObjectNumber Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_NR Metadata_STAIN Metadata_STAIN Metadata_Series Metadata_X Metadata_X Metadata_Y Metadata_Y Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_DAB_OBJECT.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_DAB_OBJECT.txt | tail -n +2 >> ${STAIN}_DAB_OBJECT.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_DAB_OBJECT.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_DAB_OBJECT.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echocyan "Collecting data for DAB NUCLEI." + + echo "ImageNumber ObjectNumber Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_NR Metadata_STAIN Metadata_STAIN Metadata_Series Metadata_X Metadata_X Metadata_Y Metadata_Y Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_NUCLEI_DAB.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_NUCLEI_DAB.txt | tail -n +2 >> ${STAIN}_NUCLEI_DAB.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_NUCLEI_DAB.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_NUCLEI_DAB.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echocyan "Collecting data for NUCLEI." + + echo "ImageNumber ObjectNumber Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_NR Metadata_STAIN Metadata_STAIN Metadata_Series Metadata_X Metadata_X Metadata_Y Metadata_Y" > ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_NUCLEI_HEMATOXYLIN.txt | tail -n +2 >> ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt + gzip -fv $AE_NUM/cp_output/${STAIN}_NUCLEI_HEMATOXYLIN.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -fv ${STAIN}_NUCLEI_HEMATOXYLIN.results.txt + + echo "" + echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echobold "Wow. I'm all done buddy. What a job! let's have a beer!" + date + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message \ No newline at end of file diff --git a/utilities/slideResultCollectorSMA.sh b/utilities/slideResultCollectorSMA.sh new file mode 100755 index 0000000..faf4f93 --- /dev/null +++ b/utilities/slideResultCollectorSMA.sh @@ -0,0 +1,323 @@ +#!/bin/bash + +### Creating display functions +### Setting colouring +NONE='\033[00m' +BOLD='\033[1m' +ITALIC='\033[3m' +OPAQUE='\033[2m' +FLASHING='\033[5m' +UNDERLINE='\033[4m' + +RED='\033[01;31m' +GREEN='\033[01;32m' +YELLOW='\033[01;33m' +PURPLE='\033[01;35m' +CYAN='\033[01;36m' +WHITE='\033[01;37m' +### Regarding changing the 'type' of the things printed with 'echo' +### Refer to: +### - http://askubuntu.com/questions/528928/how-to-do-underline-bold-italic-strikethrough-color-background-and-size-i +### - http://misc.flogisoft.com/bash/tip_colors_and_formatting +### - http://unix.stackexchange.com/questions/37260/change-font-in-echo-command + +### echo -e "\033[1mbold\033[0m" +### echo -e "\033[3mitalic\033[0m" ### THIS DOESN'T WORK ON MAC! +### echo -e "\033[4munderline\033[0m" +### echo -e "\033[9mstrikethrough\033[0m" +### echo -e "\033[31mHello World\033[0m" +### echo -e "\x1B[31mHello World\033[0m" + +function echobold { #'echobold' is the function name + echo -e "${BOLD}${1}${NONE}" # this is whatever the function needs to execute, note ${1} is the text for echo +} +function echoitalic { + echo -e "${ITALIC}${1}${NONE}" +} +function echocyan { + echo -e "${CYAN}${1}${NONE}" +} + +function echonooption { + echo -e "${OPAQUE}${RED}${1}${NONE}" +} + +# errors +function echoerrorflash { + echo -e "${RED}${BOLD}${FLASHING}${1}${NONE}" +} +function echoerror { + echo -e "${RED}${1}${NONE}" +} + +# errors no option +function echoerrornooption { + echo -e "${YELLOW}${1}${NONE}" +} +function echoerrorflashnooption { + echo -e "${YELLOW}${BOLD}${FLASHING}${1}${NONE}" +} + +### MESSAGE FUNCTIONS +script_copyright_message() { + echo "" + THISYEAR=$(date +'%Y') + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echo "+ The MIT License (MIT) +" + echo "+ Copyright (c) 2015-${THISYEAR} Sander W. van der Laan +" + echo "+ +" + echo "+ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +" + echo "+ associated documentation files (the \"Software\"), to deal in the Software without restriction, +" + echo "+ including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +" + echo "+ and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +" + echo "+ subject to the following conditions: +" + echo "+ +" + echo "+ The above copyright notice and this permission notice shall be included in all copies or substantial +" + echo "+ portions of the Software. +" + echo "+ +" + echo "+ THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +" + echo "+ NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +" + echo "+ NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES +" + echo "+ OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN +" + echo "+ CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +" + echo "+ +" + echo "+ Reference: http://opensource.org. +" + echo "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +} +script_arguments_error() { + echoerror "$1" # ERROR MESSAGE + echoerror "- Argument #1 indicates the stain used." + echoerror "" + echoerror "An example command would be: slideResultCollectorSMA.sh [arg1: STAIN] " + echoerror "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + # The wrong arguments are passed, so we'll exit the script now! + exit 1 +} + +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" +echobold " slideResultCollector for SMA" +echo "" +echoitalic "* Written by : Sander W. van der Laan" +echoitalic "* E-mail : s.w.vanderlaan-2@umcutrecht.nl" +echoitalic "* Last update : 2019-01-23" +echoitalic "* Version : 1.0.1" +echo "" +echoitalic "* Description : This script will collect data for a object quantification," +echoitalic " such as DAB based SMA." +echo "" +echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + +# filename: SMA_DAB_object.txt +# ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number +# +# filename: SMA_nuclei_DAB.txt +# ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number +# +# filename: SMA_HE_object.txt +# ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number +# +# filename: SMA_nuclei_HE.txt +# ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number +# +# filename: SMA_Image.txt +# AreaOccupied_AreaOccupied_DAB_object_yellow AreaOccupied_AreaOccupied_HE_object_green AreaOccupied_AreaOccupied_nuclei_DAB_red AreaOccupied_AreaOccupied_nuclei_HE_blue AreaOccupied_Perimeter_DAB_object_yellow AreaOccupied_Perimeter_HE_object_green AreaOccupied_Perimeter_nuclei_DAB_red AreaOccupied_Perimeter_nuclei_HE_blue AreaOccupied_TotalArea_DAB_object_yellow AreaOccupied_TotalArea_HE_object_green AreaOccupied_TotalArea_nuclei_DAB_red AreaOccupied_TotalArea_nuclei_HE_blue Channel_EntropyImage Channel_Original Count_DAB_object Count_HE_object Count_nuclei_DAB Count_nuclei_HE ExecutionTime_01Images ExecutionTime_02Metadata ExecutionTime_03NamesAndTypes ExecutionTime_04Groups ExecutionTime_05ColorToGray ExecutionTime_06Morph ExecutionTime_07UnmixColors ExecutionTime_08IdentifyPrimaryObjects ExecutionTime_09IdentifyPrimaryObjects ExecutionTime_10MaskImage ExecutionTime_11MaskImage ExecutionTime_12IdentifyPrimaryObjects ExecutionTime_13IdentifyPrimaryObjects ExecutionTime_14OverlayOutlines ExecutionTime_15SaveImages ExecutionTime_16MeasureImageAreaOccupied FileName_EntropyImage FileName_Original Frame_EntropyImage Frame_Original Group_Index Group_Number Height_EntropyImage Height_Original ImageNumber ImageSet_ImageSet MD5Digest_EntropyImage MD5Digest_Original Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_STAIN Metadata_Series Metadata_X Metadata_Y ModuleError_01Images ModuleError_02Metadata ModuleError_03NamesAndTypes ModuleError_04Groups ModuleError_05ColorToGray ModuleError_06Morph ModuleError_07UnmixColors ModuleError_08IdentifyPrimaryObjects ModuleError_09IdentifyPrimaryObjects ModuleError_10MaskImage ModuleError_11MaskImage ModuleError_12IdentifyPrimaryObjects ModuleError_13IdentifyPrimaryObjects ModuleError_14OverlayOutlines ModuleError_15SaveImages ModuleError_16MeasureImageAreaOccupied PathName_EntropyImage PathName_Original Scaling_EntropyImage Scaling_Original Series_EntropyImage Series_Original Threshold_FinalThreshold_DAB_object Threshold_FinalThreshold_HE_object Threshold_FinalThreshold_nuclei_DAB Threshold_FinalThreshold_nuclei_HE Threshold_OrigThreshold_DAB_object Threshold_OrigThreshold_HE_object Threshold_OrigThreshold_nuclei_DAB Threshold_OrigThreshold_nuclei_HE Threshold_SumOfEntropies_DAB_object Threshold_SumOfEntropies_HE_object Threshold_SumOfEntropies_nuclei_DAB Threshold_SumOfEntropies_nuclei_HE Threshold_WeightedVariance_DAB_object Threshold_WeightedVariance_HE_object Threshold_WeightedVariance_nuclei_DAB Threshold_WeightedVariance_nuclei_HE URL_EntropyImage URL_Original Width_EntropyImage Width_Original +# +# filename: SMA_Experiment.txt +# Key Value + + +### REQUIRED | GENERALS +STAIN="$1" # Depends on arg1 + +### START of if-else statement for the number of command-line arguments passed ### +if [[ $# -lt 1 ]]; then + echo "Oh, computer says no! Number of arguments found "$#"." + script_arguments_error "You must supply correct arguments when running a *** RESULT CONCATENATOR ***!" + +else + + echo "All arguments are passed and correct. We will collect data for stain: [ $STAIN ] ." + + echo "" + echocyan "Collecting data for DAB OBJECTS." +# filename: SMA_DAB_object.txt +# ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number + + echo "ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_DAB_OBJECT.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_DAB_object.txt | tail -n +2 >> ${STAIN}_DAB_OBJECT.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_DAB_object.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_DAB_OBJECT.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_DAB_OBJECT.results.txt + + echo "" + echocyan "Collecting data for DAB NUCLEI." + # filename: SMA_nuclei_DAB.txt + # ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number + + echo "ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_NUCLEI_DAB.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_nuclei_DAB.txt | tail -n +2 >> ${STAIN}_NUCLEI_DAB.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_nuclei_DAB.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_NUCLEI_DAB.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_NUCLEI_DAB.results.txt + + echo "" + echocyan "Collecting data for HE OBJECTS." + # filename: SMA_HE_object.txt + # ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number + + echo "ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_HE_OBJECT.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_HE_object.txt | tail -n +2 >> ${STAIN}_HE_OBJECT.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_HE_object.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_HE_OBJECT.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_HE_OBJECT.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_HE_OBJECT.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_HE_OBJECT.results.txt + + echo "" + echocyan "Collecting data for HE NUCLEI." + # filename: SMA_nuclei_HE.txt + # ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number + + echo "ImageNumber ObjectNumber Location_Center_X Location_Center_Y Number_Object_Number" > ${STAIN}_NUCLEI_HE.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_nuclei_HE.txt | tail -n +2 >> ${STAIN}_NUCLEI_HE.results.txt + gzip -fv $AE_NUM/cp_output/${STAIN}_nuclei_HE.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_NUCLEI_HE.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_NUCLEI_HE.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_NUCLEI_HE.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -fv ${STAIN}_NUCLEI_HE.results.txt + + echo "" + echocyan "Collecting data for IMAGE." + # filename: SMA_Image.txt + # AreaOccupied_AreaOccupied_DAB_object_yellow AreaOccupied_AreaOccupied_HE_object_green AreaOccupied_AreaOccupied_nuclei_DAB_red AreaOccupied_AreaOccupied_nuclei_HE_blue AreaOccupied_Perimeter_DAB_object_yellow AreaOccupied_Perimeter_HE_object_green AreaOccupied_Perimeter_nuclei_DAB_red AreaOccupied_Perimeter_nuclei_HE_blue AreaOccupied_TotalArea_DAB_object_yellow AreaOccupied_TotalArea_HE_object_green AreaOccupied_TotalArea_nuclei_DAB_red AreaOccupied_TotalArea_nuclei_HE_blue Channel_EntropyImage Channel_Original Count_DAB_object Count_HE_object Count_nuclei_DAB Count_nuclei_HE ExecutionTime_01Images ExecutionTime_02Metadata ExecutionTime_03NamesAndTypes ExecutionTime_04Groups ExecutionTime_05ColorToGray ExecutionTime_06Morph ExecutionTime_07UnmixColors ExecutionTime_08IdentifyPrimaryObjects ExecutionTime_09IdentifyPrimaryObjects ExecutionTime_10MaskImage ExecutionTime_11MaskImage ExecutionTime_12IdentifyPrimaryObjects ExecutionTime_13IdentifyPrimaryObjects ExecutionTime_14OverlayOutlines ExecutionTime_15SaveImages ExecutionTime_16MeasureImageAreaOccupied FileName_EntropyImage FileName_Original Frame_EntropyImage Frame_Original Group_Index Group_Number Height_EntropyImage Height_Original ImageNumber ImageSet_ImageSet MD5Digest_EntropyImage MD5Digest_Original Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_STAIN Metadata_Series Metadata_X Metadata_Y ModuleError_01Images ModuleError_02Metadata ModuleError_03NamesAndTypes ModuleError_04Groups ModuleError_05ColorToGray ModuleError_06Morph ModuleError_07UnmixColors ModuleError_08IdentifyPrimaryObjects ModuleError_09IdentifyPrimaryObjects ModuleError_10MaskImage ModuleError_11MaskImage ModuleError_12IdentifyPrimaryObjects ModuleError_13IdentifyPrimaryObjects ModuleError_14OverlayOutlines ModuleError_15SaveImages ModuleError_16MeasureImageAreaOccupied PathName_EntropyImage PathName_Original Scaling_EntropyImage Scaling_Original Series_EntropyImage Series_Original Threshold_FinalThreshold_DAB_object Threshold_FinalThreshold_HE_object Threshold_FinalThreshold_nuclei_DAB Threshold_FinalThreshold_nuclei_HE Threshold_OrigThreshold_DAB_object Threshold_OrigThreshold_HE_object Threshold_OrigThreshold_nuclei_DAB Threshold_OrigThreshold_nuclei_HE Threshold_SumOfEntropies_DAB_object Threshold_SumOfEntropies_HE_object Threshold_SumOfEntropies_nuclei_DAB Threshold_SumOfEntropies_nuclei_HE Threshold_WeightedVariance_DAB_object Threshold_WeightedVariance_HE_object Threshold_WeightedVariance_nuclei_DAB Threshold_WeightedVariance_nuclei_HE URL_EntropyImage URL_Original Width_EntropyImage Width_Original + + echo "AreaOccupied_AreaOccupied_DAB_object_yellow AreaOccupied_AreaOccupied_HE_object_green AreaOccupied_AreaOccupied_nuclei_DAB_red AreaOccupied_AreaOccupied_nuclei_HE_blue AreaOccupied_Perimeter_DAB_object_yellow AreaOccupied_Perimeter_HE_object_green AreaOccupied_Perimeter_nuclei_DAB_red AreaOccupied_Perimeter_nuclei_HE_blue AreaOccupied_TotalArea_DAB_object_yellow AreaOccupied_TotalArea_HE_object_green AreaOccupied_TotalArea_nuclei_DAB_red AreaOccupied_TotalArea_nuclei_HE_blue Channel_EntropyImage Channel_Original Count_DAB_object Count_HE_object Count_nuclei_DAB Count_nuclei_HE ExecutionTime_01Images ExecutionTime_02Metadata ExecutionTime_03NamesAndTypes ExecutionTime_04Groups ExecutionTime_05ColorToGray ExecutionTime_06Morph ExecutionTime_07UnmixColors ExecutionTime_08IdentifyPrimaryObjects ExecutionTime_09IdentifyPrimaryObjects ExecutionTime_10MaskImage ExecutionTime_11MaskImage ExecutionTime_12IdentifyPrimaryObjects ExecutionTime_13IdentifyPrimaryObjects ExecutionTime_14OverlayOutlines ExecutionTime_15SaveImages ExecutionTime_16MeasureImageAreaOccupied FileName_EntropyImage FileName_Original Frame_EntropyImage Frame_Original Group_Index Group_Number Height_EntropyImage Height_Original ImageNumber ImageSet_ImageSet MD5Digest_EntropyImage MD5Digest_Original Metadata_FileLocation Metadata_Frame Metadata_NR Metadata_STAIN Metadata_Series Metadata_X Metadata_Y ModuleError_01Images ModuleError_02Metadata ModuleError_03NamesAndTypes ModuleError_04Groups ModuleError_05ColorToGray ModuleError_06Morph ModuleError_07UnmixColors ModuleError_08IdentifyPrimaryObjects ModuleError_09IdentifyPrimaryObjects ModuleError_10MaskImage ModuleError_11MaskImage ModuleError_12IdentifyPrimaryObjects ModuleError_13IdentifyPrimaryObjects ModuleError_14OverlayOutlines ModuleError_15SaveImages ModuleError_16MeasureImageAreaOccupied PathName_EntropyImage PathName_Original Scaling_EntropyImage Scaling_Original Series_EntropyImage Series_Original Threshold_FinalThreshold_DAB_object Threshold_FinalThreshold_HE_object Threshold_FinalThreshold_nuclei_DAB Threshold_FinalThreshold_nuclei_HE Threshold_OrigThreshold_DAB_object Threshold_OrigThreshold_HE_object Threshold_OrigThreshold_nuclei_DAB Threshold_OrigThreshold_nuclei_HE Threshold_SumOfEntropies_DAB_object Threshold_SumOfEntropies_HE_object Threshold_SumOfEntropies_nuclei_DAB Threshold_SumOfEntropies_nuclei_HE Threshold_WeightedVariance_DAB_object Threshold_WeightedVariance_HE_object Threshold_WeightedVariance_nuclei_DAB Threshold_WeightedVariance_nuclei_HE URL_EntropyImage URL_Original Width_EntropyImage Width_Original" > ${STAIN}_Image.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_Image.txt | tail -n +2 >> ${STAIN}_Image.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_Image.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_Image.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_Image.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_Image.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_Image.results.txt + + echo "" + echocyan "Collecting data for Experiment." + # filename: SMA_Experiment.txt + # Key Value + + echo "Key Value" > ${STAIN}_Experiment.results.txt + + for AE_NUM in $(ls -d AE*); do + echo "* checking data from $AE_NUM ..." + cat $AE_NUM/cp_output/${STAIN}_Experiment.txt | tail -n +2 >> ${STAIN}_Experiment.results.txt + gzip -vf $AE_NUM/cp_output/${STAIN}_Experiment.txt + done + + echo "" + echo "Let's get a-head:" + head ${STAIN}_Experiment.results.txt + + echo "" + echo "Let's tail that shizzle:" + tail ${STAIN}_Experiment.results.txt + + echo "" + echo "Let's get a count of lines:" + cat ${STAIN}_Experiment.results.txt | tail -n +2 | wc -l + + echo "" + echo "Saving space by gzipping that shizzle ..." + gzip -vf ${STAIN}_Experiment.results.txt + + echo "" + echo "++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++" + echobold "Wow. I'm all done buddy. What a job! let's have a beer!" + date + +### END of if-else statement for the number of command-line arguments passed ### +fi + +script_copyright_message \ No newline at end of file diff --git a/utilities/unprocessor.py b/utilities/unprocessor.py new file mode 100755 index 0000000..9cc3543 --- /dev/null +++ b/utilities/unprocessor.py @@ -0,0 +1,12 @@ +import os +import shutil +import glob +from sys import argv + +stainPath = argv[1] + +for i in glob.glob(stainPath + "_[0-9]*"): + for j in glob.glob(i+"/*"): + shutil.move(j, stainPath) + print(j) + os.rmdir(i)