The data associated to the DeepCytometer project (https://github.com/MRC-Harwell/cytometer) is available from Zenodo (doi: 10.5281/zenodo.5137433 and 10.5281/zenodo.5149005).
The histology and mouse measures were generated as part of the Small et al. 2018 study:
Small et al. "Regulatory variants at KLF14 influence type 2 diabetes risk via a female-specific effect on adipocyte size and body composition". Nature Genetics, 50:572–580, 2018.
The hand traced data set, colour maps, and automatic segmentations were generated for the Casero et al. 2021 paper:
Casero et al. "Phenotyping of Klf14 mouse white adipose tissue enabled by whole slide segmentation with deep neural networks". bioRxiv, 2021. doi: 10.1101/2021.06.03.444997.
To develop and evaluate our methods we used Klf14tm1(KOMP)Vlcg C57BL/6NTac (B6NTac) mice tissue samples and additional data generated as part of the Small et al. 2018 study(Small et al. 2018). It should be noted that the single exon Klf14 gene is imprinted and only expressed from the maternally inherited allele(Parker-Katiraee et al. 2007). This was taken into account by (Small et al. 2018) by crossing a Het parent with a WT parent, so that each offspring inherited a WT allele from the WT parent, and the Klf14 gene knockout or a WT allele from the other parent (from the father, PAT, or the mother, MAT). We also take Klf14 imprinting into account by using as controls the PAT mice and comparing them to the MAT WT and MAT Het (or functional KO, FKO) mice.
We used a total of 76 Klf14-B6NTac mice (nfemale=nmale=38), of which 20 mice from the Control and FKO groups were used for training and testing the DeepCytometer pipeline, as well as the hand traced population experiment (summary in Table MICE).
The histopathology screen involved fixing, processing and embedding in wax, sectioning and staining with Hematoxylin and Eosin (H&E) both inguinal subcutaneous and gonadal adipose depots. For paraffin-embedded sections, all samples were fixed in 10% neutral buffered formalin (Surgipath) for at least 48 hours at RT and processed using an Excelsior™ AS Tissue Processor (Thermo Scientific). Samples were embedded in molten paraffin wax and 8 μm sections were cut through the respective depots using a Finesse™ ME+ microtome (Thermo Scientific). Sampling was conducted at 2sxns per slide, 3 slides per depot block onto simultaneous charged slides, stained with haematoxylin Gill 3 and eosin (Thermo scientific) and scanned using an NDP NanoZoomer Digital pathology scanner (RS C10730 Series; Hamamatsu). Body weight (BW) and depot weight (DW) were measured with Satorius BAL7000 scales.
For cell area quantification, we applied DeepCytometer v8 to 75 inguinal subcutaneous and 72 gonadal whole histology slides with DeepCytometer (with the Corrected method), including the 20 slides sampled for the hand-traced data set, corresponding to 73 females and 74 males, to produce 2,560,067 subcutaneous and 2,467,686 gonadal cells (on average, 34,134 and 34,273 cells per slide, respectively).
Full segmentation of all whole slides was performed with script klf14_b6ntac_exp_0106_full_slide_pipeline_v8.py. In this case, the segmentation contours were grouped by tiles in the output AIDA annotation .json file (one contour per cell, one file per slide).
Non-white adipocyte contours were filtered out, and white adipocyte contours were aggregated into an AIDA annotation .json file with a single tile with script klf14_b6ntac_exp_0106_annotations_postprocessing_v8.py (one contour per cell, one file per slide).
Casero et al. (2021) "DeepCytometer pipeline parameter files, Klf14 mouse white adipose tissue histology and hand-traced training contours" (doi: 10.5281/zenodo.5137433)
Weights, colourmaps, etc. necessary to run the pipeline (v8, with mode colour correction). This is the version of the pipeline described in the paper.
There are 10 weight files per convolutional neural network (CNN), corresponding to 10-fold cross-validation
klf14_b6ntac_exp_0086_cnn_dmap_model_fold_[0..9].h5:
Keras weights for the EDT CNN (Histology to Euclidean Distance Transform regression)klf14_b6ntac_exp_0089_cnn_segmentation_correction_overlapping_scaled_contours_model_fold_[0..9].h5:
Keras weights for the Correction CNN (Segmentation Correction regression)klf14_b6ntac_exp_0091_cnn_contour_after_dmap_model_fold_[0..9].h5:
Keras weights for the Contour CNN (EDT to Contour detection)klf14_b6ntac_exp_0095_cnn_tissue_classifier_fcn_model_fold_[0..9].h5:
Keras weights for the Tissue CNN (Pixel-wise tissue classifier)klf14_b6ntac_exp_0094_generate_extra_training_images.pickle:
training dataset description- 'file_list':
list of SVG files with hand-traced contours for network training. Each SVG file has a corresponding TIFF file with the histology used for segmentation - 'idx_test':
10 lists with file indices for testing in 10-fold cross-validation - 'idx_train':
10 lists with file indices for training in 10-fold cross-validation - 'fold_seed':
seed number used for the random number generator to assign file indices to folds
- 'file_list':
klf14_b6ntac_exp_0098_filename_area2quantile.npz:
quantile colour maps calculated inklf14_b6ntac_exp_0098_full_slide_size_analysis_v7.pyusing the whole Klf14 data set with v7 of the pipeline, and used in earlier experiments, including some where v8 of the pipeline was used for segmentation.klf14_b6ntac_exp_0106_filename_area2quantile_v8.npz:
quantile colour maps calculated inklf14_b6ntac_exp_0106_full_slide_pipeline_v8.pyusing the whole Klf14 data set with v8 of the pipeline, and used in later experiments.klf14_training_colour_histogram.npz:
statistics from Klf14 histology images to be used in colour correction- 'xbins_edge', 'xbins':
edges and centres of the bins used for histogram calculations - 'hist_r_q1', 'hist_r_q2', 'hist_r_q3'
- 'hist_g_q1', 'hist_g_q2', 'hist_g_q3'
- 'hist_b_q1', 'hist_b_q2', 'hist_b_q3':
density quartiles (Q1, Q2, Q3) for RGB channels for each bin the histogram - 'mode_r', 'mode_g', 'mode_b':
modes for RGB channels (this corresponds to the most typical background colour in the histology images) - 'mean_l', 'mean_a', 'mean_b':
mean intensity for Lab channels of the image - 'std_l', 'std_a', 'std_b':
intensity standard deviations for Lab channels of the image
- 'xbins_edge', 'xbins':
klf14_exp_0112_training_colour_histogram.npz:
other statistics from Klf14 histology images to be used in colour correction- 'p':
vector of quantile values used in ECDF calculations - 'val_r_klf14', 'val_g_klf14', 'val_b_klf14':
all intensity values for the RGB channels of Klf14 training images that contain at least a white adipocyte - 'f_ecdf_to_val_r_klf14', 'f_ecdf_to_val_g_klf14', 'f_ecdf_to_val_b_klf14':
linear interpolation function that maps ECDF quantiles to intensity values in the Klf14 training data set. These functions can be used together with intensity->quantile interpolation functions calculated for a new histology image to perform histogram matching colour correction - 'mean_klf14', 'std_klf14':
mean and standard deviation of the 'val_r_klf14', 'val_g_klf14', 'val_b_klf14' vectors
- 'p':
There are also weight files for the pipeline trained with all the data, instead of the 10-fold cross-validation partition. These were not used for the paper, but could be useful for future experiments
klf14_b6ntac_exp_0101_cnn_dmap_model.h5:
Keras weights for the EDT CNN (Histology to Euclidean Distance Transform regression)klf14_b6ntac_exp_0104_cnn_segmentation_correction_overlapping_scaled_contours_model.h5:
Keras weights for the Correction CNN (Segmentation Correction regression)klf14_b6ntac_exp_0102_cnn_contour_after_dmap_model.h5:
Keras weights for the Contour CNN (EDT to Contour detection)klf14_b6ntac_exp_0103_cnn_tissue_classifier_fcn_model.h5:
Keras weights for the Tissue CNN (Pixel-wise tissue classifier)
165 H&E histology whole slides from Hamamatsu scanner (.ndpi).
Mice metadata, training/testing data sets for the pipeline, intermediate files created during training, and neural network weights for multiple experiments.
klf14_b6ntac_meta_info.csv:
Klf14 mice metadata- Animal Identifier, id: unique ID for each mouse
- ko_parent: heterozygous parent of origin for the KO allele (father, PAT or mother, MAT)
- sex: female or male
- genotype: wild type (KLF14-KO:WT) or heterozygous (KLF14-KO:Het)
- BW: body weight (g)
- SC: subcutaneous depot weight (g)
- gWAT: gonadal depot weight (g)
- Liver: livel weight (g)
- cull_age: age at time of culling (days)
- BW_alive: body weight measured before culling
- BW_alive_date: age at time of BW_alive measure
- mother: unique ID for mouse's mother
- mother_genotype: mouse's mother genotype
klf14_b6ntac_training:
Directory with hand-traced segmentations of training histology windows. 131 windows sampled from 20 whole slides, plus hand-traced contours that were used for training DeepCytometer and compute population distributions. These segmentations were used for CNN training, but note that there's a cleaned-up version of these data below, and it was the cleaned-up version that was used for the paper experimentsndpifile_row_YYYYYY_col_XXXXXX[.tif/.xcf/.svg]:- ndpifile: name of the whole slide file (e.g.
KLF14-B6NTAC 36.1c PAT 98-16 C1 - 2016-02-11 10.45.00) - row_YYYYYY: Y-coordinate of the top-left corner of the sampling window, in pixels
- col_XXXXXX: X-coordinate of the top-left corner of the sampling window, in pixels
- .tif: TIFF file with the histology sampling window
- .xcf: Gimp file with the histology and hand-traced contours (the contours were drawn in Gimp)
- .svg: SVG (Scalable Vector Graphics) that contains the hand-traced contours in the XCF file
- ndpifile: name of the whole slide file (e.g.
klf14_b6ntac_training_v2:
Same asklf14_b6ntac_training, but the hand-traced data set was cleaned up to remove small contours of dubious cells, or cells that are fully overlapped by othersklf14_b6ntac_training_non_overlap: Directory with intermediate images to train the networks. These images are generated by scriptklf14_b6ntac_training_non_overlapklf14_b6ntac_training_augmented: Directory with intermediate images used to train the networks (using augmentation to reduce overfitting). These images are generated by scriptklf14_b6ntac_exp_0078_generate_augmented_training_images.pyklf14_b6ntac_seg: Deprecated. Directory to store whole slide coarse segmentations in old experiments (e.g.klf14_b6ntac_exp_0076_generate_training_images.py). Of little interest for most usersklf14_b6ntac_results:
Deprecated. Directory to store miscellanea output from some experiments. Of little interest for most users
Casero et al. (2021). "Klf14 mouse white adipose tissue histology DeepZoom files and AIDA annotations for visualisation of DeepCytometer white adipocyte segmentations" (doi: 10.5281/zenodo.5149005)
Histology images converted to DeepZoom so that they can be visualised with AIDA.
To use this, decompress this file and put the resulting images directory in your AIDA/dist/data/ directory.
White adipocyte segmentations in AIDA annotation .json files (one contour per cell, one file per whole slide). Each slide has the following files:
SLIDENAME.json:
Soft link to the annotations file that we want to associate to slideSLIDENAME.ndpi, e.g.SLIDENAME=KLF14-B6NTAC-PAT-39.2d 454-16 B1 - 2016-03-17 12.16.06SLIDENAME.lock:
Empty file used to tell the pipeline thatSLIDENAME.ndpihas already been processed or is being currently processedSLIDENAME_coarse_mask.npz:
File with the coarse tissue segmentation ofSLIDENAME.ndpiand the internal state of the pipeline (execution times, steps, etc)SLIDENAME_exp_0106_auto.json:
Annotations (all segmentations without filtering from the Auto algorithm, i.e. segmentation without object overlap). Contours are grouped by the tile they were processed inSLIDENAME_exp_0106_auto_aggregated.json:
Filtered annotations (non-white adipocytes removed) of the Auto algorithm. All contours aggregated into a single tileSLIDENAME_exp_0106_corrected.json:
Annotations (all segmentations without filtering from the Corrected algorithm, i.e. segmentation with object overlap). Contours are grouped by the tile they were processed inSLIDENAME_exp_0106_corrected_aggregated.json:
Filtered annotations (non-white adipocytes removed) of the Corrected algorithm. All contours aggregated into a single tile
To use this, decompress this file and put the resulting annotations directory in your AIDA/dist/data/ directory.