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Gilbert, Andrew
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# med-seg | ||
# Medical Segmentation | ||
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 | ||
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A repository for training segmentation algorithms for medical images. This repository is primarily developed for | ||
segmentation of apical four and two chamber echocardiography images like those from the | ||
[Camus](https://www.creatis.insa-lyon.fr/Challenge/camus/) and | ||
[EchoNet](https://echonet.github.io/dynamic/) datasets as well as other data with similar structure. | ||
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## Install | ||
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Clone this repo, create a new environment and install requirements using `pip install -r requirements.txt` | ||
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## Training | ||
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Use [train.py](train.py) to train a new model. | ||
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Run `python train.py -h` to see all available options (defined in [base_options](options/base_options.py) and | ||
[train_options](options/train_options.py)). | ||
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The main options that need to be defined are: | ||
- **dataroot**: the path to the data. This should be a directory with subfolders for each phase. At least "train" and | ||
"val" and optionally other phases (e.g. "test"). Within each phase folder should be a "images" folder and a "labels" | ||
folder. Each of these should contain pngs which have each training sample. The filenames in each directory should | ||
match. | ||
- **name**: The name of this training run. | ||
- **experiment**: The experiment to group this run into. | ||
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See the provided help string for information on other options. | ||
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## Inference | ||
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Use [inference.py](inference.py) to evaluate a trained model. | ||
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Run `python inference.py -h` to see available options. The main options match those above. | ||
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## Notes | ||
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Some parts of the code are specifically adapted for apical segmentation of the left ventricle | ||
(e.g. the [metrics](evaluators/__init__.py)) which assume | ||
specific regions are associated with specific classes. These will need to be updated for a new segmentation task. | ||
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Some of the structure/code from this repository is based on | ||
[CycleGAN_and_pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). |
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