Region based nnunet scripts

exclude_M0_M12_comparison.yml

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+# PSIR
+- sub-mon118_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/43
+- sub-mon006_ses-M0   # FOV covers the whole brain. The SC has only C1-C6; see https://github.com/ivadomed/canproco/issues/22#issue-1568568207
+- sub-mon009_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758684880
+- sub-mon032_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758686509
+- sub-mon097_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758686772
+- sub-mon113_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1760175861
+- sub-mon148_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758686965
+- sub-mon152_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1760248384
+- sub-mon168_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758687112
+- sub-mon191_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758687352
+- sub-van134_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1761698648
+- sub-van135_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1761702591
+- sub-van171_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1761910863
+- sub-van176_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758687602
+- sub-van181_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1767235494
+- sub-van201_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1767238882
+- sub-van206_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758688851
+- sub-van207_ses-M0   # No disc labeling file : impossible to do it : https://github.com/ivadomed/canproco/issues/53#issuecomment-1761973064
+- sub-tor014_ses-M0   # Poor data quality and no disc labeling file; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1760420705
+- sub-tor133_ses-M0   # Poor data quality; see https://github.com/ivadomed/canproco/issues/53#issuecomment-1758689928
+# STIR
+- sub-cal149_ses-M0   # FOV does not cover the whole SC in sagittal plane; see https://github.com/ivadomed/canproco/issues/22#issue-1568568207
+# Similar accross timepoints
+- sub-edm005_ses-M0
+- sub-edm031_ses-M0
+- sub-edm035_ses-M0
+- sub-edm054_ses-M0
+- sub-edm065_ses-M0
+- sub-edm088_ses-M0
+- sub-mon002_ses-M0
+- sub-mon003_ses-M0
+- sub-mon005_ses-M0
+- sub-mon006_ses-M0
+- sub-mon007_ses-M0
+- sub-mon009_ses-M0
+- sub-mon010_ses-M0
+- sub-mon011_ses-M0
+- sub-mon014_ses-M0
+- sub-mon031_ses-M0
+- sub-mon032_ses-M0
+- sub-mon041_ses-M0
+- sub-mon052_ses-M0
+- sub-mon060_ses-M0
+- sub-tor010_ses-M0
+- sub-tor012_ses-M0
+- sub-tor014_ses-M0
+- sub-tor016_ses-M0
+- sub-tor021_ses-M0
+- sub-tor029_ses-M0
+- sub-tor036_ses-M0
+- sub-tor038_ses-M0
+- sub-tor039_ses-M0
+- sub-tor043_ses-M0
+- sub-tor044_ses-M0
+- sub-tor051_ses-M0
+# Subjects only present at M12
+- sub-cal064_ses-M12
+- sub-edm161_ses-M12
+- sub-mon164_ses-M12
+# Subject with 4 dimensions
+- sub-cal123_ses-M12
+# Subject only present at M0
+- sub-cal080_ses-M0
+- sub-cal113_ses-M0
+- sub-cal125_ses-M0
+- sub-cal130_ses-M0
+- sub-cal131_ses-M0
+- sub-cal136_ses-M0
+- sub-cal140_ses-M0
+- sub-cal151_ses-M0
+- sub-cal153_ses-M0
+- sub-cal154_ses-M0
+- sub-cal156_ses-M0
+- sub-cal162_ses-M0
+- sub-cal173_ses-M0
+- sub-cal185_ses-M0
+- sub-cal191_ses-M0
+- sub-cal197_ses-M0
+- sub-cal198_ses-M0
+- sub-cal199_ses-M0
+- sub-cal200_ses-M0
+- sub-cal206_ses-M0
+- sub-cal207_ses-M0
+- sub-cal209_ses-M0
+- sub-cal210_ses-M0
+- sub-cal213_ses-M0
+- sub-cal214_ses-M0
+- sub-edm008_ses-M0
+- sub-edm010_ses-M0
+- sub-edm018_ses-M0
+- sub-edm023_ses-M0
+- sub-edm029_ses-M0
+- sub-edm034_ses-M0
+- sub-edm038_ses-M0
+- sub-edm053_ses-M0
+- sub-edm064_ses-M0
+- sub-edm067_ses-M0
+- sub-edm089_ses-M0
+- sub-edm095_ses-M0
+- sub-edm098_ses-M0
+- sub-edm107_ses-M0
+- sub-edm138_ses-M0
+- sub-edm157_ses-M0
+- sub-edm162_ses-M0
+- sub-edm164_ses-M0
+- sub-edm169_ses-M0
+- sub-edm178_ses-M0
+- sub-edm181_ses-M0
+- sub-edm183_ses-M0
+- sub-mon013_ses-M0
+- sub-mon027_ses-M0
+- sub-mon064_ses-M0
+- sub-mon107_ses-M0
+- sub-mon108_ses-M0
+- sub-mon109_ses-M0
+- sub-mon139_ses-M0
+- sub-mon163_ses-M0 # parent folder exist, missing only PSIR contrast
+- sub-mon180_ses-M0
+- sub-mon181_ses-M0
+- sub-tor004_ses-M0
+- sub-tor018_ses-M0
+- sub-tor024_ses-M0
+- sub-tor025_ses-M0
+- sub-tor031_ses-M0
+- sub-tor032_ses-M0
+- sub-tor041_ses-M0
+- sub-tor055_ses-M0
+- sub-tor063_ses-M0
+- sub-tor066_ses-M0
+- sub-tor073_ses-M0
+- sub-tor076_ses-M0
+- sub-tor077_ses-M0
+- sub-tor106_ses-M0
+- sub-tor109_ses-M0
+- sub-tor110_ses-M0
+- sub-tor112_ses-M0
+- sub-tor124_ses-M0
+- sub-tor125_ses-M0
+- sub-tor147_ses-M0
+- sub-van009_ses-M0
+- sub-van093_ses-M0
+- sub-van112_ses-M0
+- sub-van116_ses-M0
+- sub-van123_ses-M0
+- sub-van125_ses-M0
+- sub-van129_ses-M0
+- sub-van131_ses-M0
+- sub-van134_ses-M0
+- sub-van144_ses-M0
+- sub-van145_ses-M0
+- sub-van147_ses-M0
+- sub-van149_ses-M0
+- sub-van150_ses-M0
+- sub-van151_ses-M0
+- sub-van154_ses-M0
+- sub-van168_ses-M0
+- sub-van172_ses-M0
+- sub-van173_ses-M0
+- sub-van178_ses-M0
+- sub-van184_ses-M0
+- sub-van186_ses-M0
+- sub-van204_ses-M0
+- sub-van210_ses-M0
+- sub-van211_ses-M0
+- sub-van214_ses-M0
+- sub-van215_ses-M0
+- sub-van217_ses-M0
+- sub-van219_ses-M0
+- sub-van221_ses-M0
+- sub-van222_ses-M0

nnunet/README.md

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+# Installation instructions
+
+## Installation of Anima metrics
+
+Installation of 
+
+```
+cd ~
+mkdir anima/
+cd anima/
+wget -q https://github.com/Inria-Empenn/Anima-Public/releases/download/v4.2/Anima-macOS-4.2.zip # for MACOS
+unzip Anima-macOS-4.2.zip
+rm Anima-macOS-4.2.zip
+git lfs install
+git clone --depth 1 https://github.com/Inria-Visages/Anima-Scripts-Public.git
+git clone --depth 1 https://github.com/Inria-Visages/Anima-Scripts-Data-Public.git
+```
+
+Configure directories
+
+```
+cd ~
+mkdir .anima/
+touch .anima/config.txt
+
+echo "[anima-scripts]" >> .anima/config.txt
+echo "anima = ${HOME}/anima/Anima-Binaries-4.2/" >> .anima/config.txt
+echo "anima-scripts-public-root = ${HOME}/anima/Anima-Scripts-Public/" >> .anima/config.txt
+echo "extra-data-root = ${HOME}/anima/Anima-Scripts-Data-Public/" >> .anima/config.txt
+```
+
+## Installation of required libraries
+
+Create a virtual invironment: 
+~~~
+conda create -n venv_nnunet python=3.9
+~~~
+
+Activate the environment with the following command:
+~~~
+conda activate venv_nnunet
+~~~
+
+To install required libraries to train an nnUNet v2:
+
+```
+pip install -r requirements_nnunet.txt
+```
+
+Install SpinalCordToolbox 6.0 :
+
+Installation link : https://spinalcordtoolbox.com/user_section/installation.html
+
+
+# Data preparation
+
+Create the following folders:
+
+~~~
+mkdir nnUNet_raw
+mkdir nnUNet_preprocessed
+mkdir nnUNet_results
+~~~
+
+We are training a region-based nnUNet taking an image from contrasts PSIR or STIR and creating a mask with 0=background, 1=spinal cord and 2=MS lesion.
+
+Convert the data to the nnUNet format :
+
+~~~
+python convert_BIDS_to_nnunet.py --path-data /path/to/BIDS/dataset --path-out /path/to/nnUNet_raw --taskname TASK-NAME --tasknumber DATASET-ID  --contrasts PSIR,STIR --test-ratio XX --time-point ses-XX --type training --exclude-file /path/to/exclude_file.yml
+~~~
+
+> **Note**
+> The test ratio is 0.2 for 20% (train ratio is therefore 80%). For M0 images, the time point is ses-M0.
+
+To mutliply PSIR images by -1 before training and convert the data to the nnUNet format :
+
+~~~
+python convert_BIDS_to_nnunet_with_mul_PSIR.py --path-data /path/to/BIDS/dataset --path-out /path/to/nnUNet_raw --taskname TASK-NAME --tasknumber DATASET-ID  --contrasts PSIR,STIR --test-ratio XX --time-point ses-XX --type training --exclude-file /path/to/exclude_file.yml
+~~~
+
+# Model training
+
+Before training the model, nnU-Net performs data preprocessing and checks the integrity of the dataset:
+
+~~~
+export nnUNet_raw="/path/to/nnUNet_raw"
+export nnUNet_preprocessed="/path/to/nnUNet_preprocessed"
+export nnUNet_results="/path/to/nnUNet_results"
+
+nnUNetv2_plan_and_preprocess -d DATASET-ID --verify_dataset_integrity
+~~~
+
+You will get the configuration plan for all four configurations (2d, 3d_fullres, 3d_lowres, 3d_cascade_fullres).
+
+To train the model, use the following command:
+~~~
+CUDA_VISIBLE_DEVICES=XXX nnUNetv2_train DATASET-ID CONFIG FOLD --npz
+~~~
+
+> **Note**
+> Example for Dataset 101, on 2d config on fold 0: CUDA_VISIBLE_DEVICES=2 nnUNetv2_train 101 2d 0 --npz
+
+# Model inference
+
+Convert data to nnUNet format for inference using `convert_BIDS_to_nnunet.py` or `convert_BIDS_to_nnunet_with_mul_PSIR.py` with `--type=inference`.
+
+Then perform inference:
+~~~
+CUDA_VISIBLE_DEVICES=XXX nnUNetv2_predict -i /path/to/image/folder -o /path/to/predictions -d DATASET_ID -c CONFIG --save_probabilities -chk checkpoint_best.pth -f FOLD
+~~~
+
+# Inference evaluation
+
+First, convert the predictions back the BIDS format ; this only keeps the lesion segmentation and discards spinal cord segmentation :
+
+~~~
+python convert_predictions_to_BIDS.py --pred-folder /path/to/predictions --out-folder /path/to/output/folder --conversion-dict /path/to/conversion/dict
+~~~
+
+If you are converting predictions which are not the evaluations set from nnUNet, use the flag `--not-imageTs`.
+
+Then, you can evaluate the lesion prediction with Anima metrics
+
+~~~
+python evaluate_lesion_seg_prediction.py --pred-folder path/to/predictions --dataset path/to/dataset --animaPath path/to/animaSegPerfAnalyzer --output-folder path/to/output_folder
+~~~
+
+# Evaluation analysis
+
+The following Notebook `nnUNet_inference_analysis.ipynb` was used to perform analysis of the nnUNet segmentations. 
+To use it, change the path to the csv files.