A complete pipeline for BraTS 2021: RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021
This repository implement the solution of the 2021 edition of the BraTS challenge describe in our paper.
If you face any problem, please feel free to open an issue.
git clone https://github.com/Alxaline/BraTS21.git
cd BraTS21
2. Create a conda environment (recommended)
ENVNAME="BraTS21"
conda create -n $ENVNAME python==3.7.7 -y
conda activate $ENVNAME
in your favorite virtual environment:
pip install -r requirements.txt
The hierarchical structure of the data folder should be as follows:
├─ data
├─ BraTS2021 # Data provided by the BraTS 2020 competition host
├─ RSNA_ASNR_MICCAI_BraTS2021_TrainingData
├─ BraTS2021_00000
├─ BraTS2021_00000_flair.nii.gz
├─ BraTS2021_00000_seg.nii.gz
├─ BraTS2021_00000_t1.nii.gz
├─ BraTS2021_00000_t1ce.nii.gz
├─ BraTS2021_00000_t2.nii.gz
├─ BraTS2021_00002
├─ ...
├─ RSNA_ASNR_MICCAI_BraTS2021_ValidationData
├─ BraTS20_Validation_001
├─ BraTS20_Validation_001_flair.nii.gz
├─ BraTS20_Validation_001_t1.nii.gz
├─ BraTS20_Validation_001_t1ce.nii.gz
├─ BraTS20_Validation_001_t2.nii.gz
├─ BraTS2021_00013
├─ ...
| Model Nb. | Sub. ID | Method | DSC WT | DSC TC | DSC ET | DSC Mean | HD95 WT | HD95 TC | HD95 ET | HD95 Mean |
|---|---|---|---|---|---|---|---|---|---|---|
| 1) | 9715210 | U-NetV1(*) | 0.91904 | 0.86616 | 0.83454 | 0.87326 | 4.40718 | 9.39596 | 15.75011 | 9.85108 |
| 2) | 9715055 | U-NetV2(*) | 0.92349 | 0.86827 | 0.83265 | 0.87475 | 4.12874 | 10.92845 | 17.48075 | 10.84598 |
| 3) | 9715112 | U-NetV2(**) | 0.92393 | 0.87063 | 0.83997 | 0.87782 | 4.61502 | 9.34665 | 15.80434 | 9.92200 |
| 4) | 9715113 | U-NetV2(*+**) | 0.92436 | 0.87168 | 0.84000 | 0.87868 | 4.49349 | 7.71372 | 14.15743 | 8.78821 |
| 5) | 9715160 | U-NetV2(*, JL) | 0.92462 | 0.87712 | 0.83994 | 0.88056 | 4.25690 | 9.21011 | 14.16697 | 9.21133 |
| 6) | 9715224 | U-NetV2(**+(*, JL)) | 0.92457 | 0.87811 | 0.84094 | 0.88121 | 4.19442 | 7.55256 | 14.13390 | 8.62696 |
| 7) | 9715209 | U-NetV2(*+**+(*, JL)) | 0.92463 | 0.87674 | 0.83916 | 0.88018 | 4.48539 | 7.53955 | 15.75771 | 9.26088 |
Model 6 was the model selected for final test. Results provided by BraTS organization were as follows:
| DSC WT | DSC TC | DSC ET | HD95 WT | HD95 TC | HD95 ET | |
|---|---|---|---|---|---|---|
| Mean | 0.92548 | 0.87628 | 0.87122 | 4.30711 | 17.84987 | 12.23361 |
| StdDev | 0.09898 | 0.23983 | 0.18204 | 8.45388 | 71.52831 | 59.54562 |
| Median | 0.95560 | 0.95975 | 0.93153 | 1.73205 | 1.41421 | 1.00000 |
| 25quantile | 0.91378 | 0.91195 | 0.85409 | 1.00000 | 1.00000 | 1.00000 |
| 75quantile | 0.97604 | 0.97916 | 0.95977 | 4.00000 | 3.00000 | 2.00000 |
For more details on the available options:
python -m src.main_train -h
In order to perform the different models proposed in the paper, run the following commands.
After a run, in the --save_path args, a folder will be created containing :
- a 'config.yaml' file with the option used
- model weights (best and last)
- a 'Evaluation.xlsx' file with metrics result on the evaluation set
- a 'segmentations' folder containing all the generated seg on the evaluation set
with the --evaluate_end_training args, best model weight will be run on the evaluation set
For each model, a cross validation was performed.
For this each command must be run again with a different --fold arg (0, 1, 2, 3 or 4)
python -m src.main_train --train_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_TrainingData/ --save_path /data/model1/fold0 --model equiunet --norm group --act relu --width 48 --criterion dice --num_workers 4 --optimizer ranger --decay_type cosine --learning_rate 0.0003 --val_frequency 2 --log_val_metrics --evaluate_end_training --remove_outliers --epochs 150 --no_full_name --fold 0 --device 0 -vv
python -m src.main_train --train_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_TrainingData/ --save_path /data/model2/fold0 --model equiunet_assp_evo --act leakyrelu --width 48 --criterion dice --num_workers 4 --optimizer ranger --decay_type cosine --learning_rate 0.0003 --val_frequency 2 --log_val_metrics --evaluate_end_training --remove_outliers --epochs 150 --no_full_name --fold 0 --device 0 -vv
python -m src.main_train --train_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_TrainingData/ --save_path /data/model3/fold0 --model equiunet_assp_evo --act leakyrelu --width 48 --criterion dice --num_workers 4 --optimizer ranger --decay_type cosine --learning_rate 0.0003 --val_frequency 2 --log_val_metrics --evaluate_end_training --remove_outliers --epochs 150 --no_full_name --fold 0 --device 0 --seed 93 -vv
python -m src.main_train --train_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_TrainingData/ --save_path /data/model5/fold0 --model equiunet_assp_evo --act leakyrelu --width 48 --criterion jaccard --num_workers 4 --optimizer ranger --decay_type cosine --learning_rate 0.0003 --val_frequency 2 --log_val_metrics --evaluate_end_training --remove_outliers --epochs 150 --no_full_name --fold 0 --device 0 -vv
For more details on the available options:
python -m src.main_inference -h
python -m src.main_inference --config /data/model1/fold0/config.yaml /data/model1/fold1/config.yaml /data/model1/fold2/config.yaml /data/model1/fold3/config.yaml /data/model1/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model1_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model2/fold0/config.yaml /data/model2/fold1/config.yaml /data/model2/fold2/config.yaml /data/model2/fold3/config.yaml /data/model2/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model2_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model3/fold0/config.yaml /data/model3/fold1/config.yaml /data/model3/fold2/config.yaml /data/model3/fold3/config.yaml /data/model3/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model3_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model1/fold0/config.yaml /data/model1/fold1/config.yaml /data/model1/fold2/config.yaml /data/model1/fold3/config.yaml /data/model1/fold4/config.yaml /data/model3/fold0/config.yaml /data/model3/fold1/config.yaml /data/model3/fold2/config.yaml /data/model3/fold3/config.yaml /data/model3/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model4_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model5/fold0/config.yaml /data/model5/fold1/config.yaml /data/model5/fold2/config.yaml /data/model5/fold3/config.yaml /data/model5/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model5_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model3/fold0/config.yaml /data/model3/fold1/config.yaml /data/model3/fold2/config.yaml /data/model3/fold3/config.yaml /data/model3/fold4/config.yaml /data/model5/fold0/config.yaml /data/model5/fold1/config.yaml /data/model5/fold2/config.yaml /data/model5/fold3/config.yaml /data/model5/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model6_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
python -m src.main_inference --config /data/model2/fold0/config.yaml /data/model2/fold1/config.yaml /data/model2/fold2/config.yaml /data/model2/fold3/config.yaml /data/model2/fold4/config.yaml /data/model3/fold0/config.yaml /data/model3/fold1/config.yaml /data/model3/fold2/config.yaml /data/model3/fold3/config.yaml /data/model3/fold4/config.yaml /data/model5/fold0/config.yaml /data/model5/fold1/config.yaml /data/model5/fold2/config.yaml /data/model5/fold3/config.yaml /data/model5/fold4/config.yaml --test_data_path /data/RSNA_ASNR_MICCAI_BraTS2021_ValidationData --on test -vv --replace_value --cleaning_areas --save_path /data/model6_inference/ --device 0 --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta
The weights of our model for the BraTS 2021 challenge can be downloaded at https://drive.google.com/file/d/1Xt2rdD60IeEwcd8-yiMZHZkI0udcXgc7/view?usp=sharing
Unzip the final_weights_brats21.zip and put the folder in BraTS21.
Execute the following command line and replace the path of --input and output args
python -m src.main_inference --config /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor/fold0_ns/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor/fold1_ns/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor/fold2_ns/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor/fold3_ns/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor/fold4_ns/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor_jaccard/fold0/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor_jaccard/fold1/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor_jaccard/fold2/config.yaml /BraTS21/final_weights_brats21/baseline_equiunet_assp_evocor_jaccard/fold3/config.yaml /BraTS21/final_weights/baseline_equiunet_assp_evocor_jaccard/fold4/config.yaml --on test -vv --replace_value --cleaning_areas --replace_value_threshold 300 --cleaning_areas_threshold 20 --device 0 --tta --input /input --output /output --num_workers 0
Retrieve the docker image of the final model used for test :
docker pull alxaline/brats21:latest
To run on a test sample :
sudo docker run -it --rm --gpus device=0 --name run_model -v "/data/RSNA_ASNR_MICCAI_BraTS2021_TestingData/BraTS20_Testing_006":"/input" -v "/data/outputseg/":"/output" alxaline/brats21 --input /input --output /output
If you find this repository useful for your research, please cite our work
- Carré, A., Deutsch, E., Robert, C. (2022). Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_23
- Henry, T. et al. (2021). Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_30
BibTeX:
@inproceedings{carreAutomaticBrainTumor2022,
location = {Cham},
title = {Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and {EvoNorm}},
isbn = {978-3-031-09002-8},
doi = {10.1007/978-3-031-09002-8_23},
series = {Lecture Notes in Computer Science},
pages = {253--266},
booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
publisher = {Springer International Publishing},
author = {Carré, Alexandre and Deutsch, Eric and Robert, Charlotte},
editor = {Crimi, Alessandro and Bakas, Spyridon},
year = {2022},
language = {en},
keywords = {Brain tumor, Deep-learning, Segmentation},
}
@inproceedings{henryBrain2021,
location = {Cham},
title = {Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution},
isbn = {978-3-030-72084-1},
doi = {10.1007/978-3-030-72084-1_30},
series = {Lecture Notes in Computer Science},
pages = {327--339},
booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
publisher = {Springer International Publishing},
author = {Henry, Théophraste and Carré, Alexandre and Lerousseau, Marvin and Estienne, Théo and Robert, Charlotte and Paragios, Nikos and Deutsch, Eric},
editor = {Crimi, Alessandro and Bakas, Spyridon},
year = {2021},
language = {en},
keywords = {Brain tumor, Deep learning, Semantic segmentation},
}