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.

Installation

1. Get the repository

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

3. Install requirements

in your favorite virtual environment:

pip install -r requirements.txt

Data directory structure

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
	    ├─ ...

Results of the paper

On validation

Model Nb.	Sub. ID	Method	DSC WT	DSC TC	DSC ET	DSC Mean	HD95 WT	HD95 TC	HD95 ET	HD95 Mean
1)	9715210	U-Net_V1^(*)	0.91904	0.86616	0.83454	0.87326	4.40718	9.39596	15.75011	9.85108
2)	9715055	U-Net_V2^(*)	0.92349	0.86827	0.83265	0.87475	4.12874	10.92845	17.48075	10.84598
3)	9715112	U-Net_V2^(**)	0.92393	0.87063	0.83997	0.87782	4.61502	9.34665	15.80434	9.92200
4)	9715113	U-Net_V2^(+*)	0.92436	0.87168	0.84000	0.87868	4.49349	7.71372	14.15743	8.78821
5)	9715160	U-Net_V2^{(*, JL)}	0.92462	0.87712	0.83994	0.88056	4.25690	9.21011	14.16697	9.21133
6)	9715224	U-Net_V2^{(*+(, JL))}	0.92457	0.87811	0.84094	0.88121	4.19442	7.55256	14.13390	8.62696
7)	9715209	U-Net_V2^{(++(, JL))}	0.92463	0.87674	0.83916	0.88018	4.48539	7.53955	15.75771	9.26088

On test

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

Training

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)

1) U-Net_V1^(*)

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

2) U-Net_V2^(*)

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

3) U-Net_V2^(**)

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

5) U-Net_V2^{(*, JL)}

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

Inference

For more details on the available options:

python -m src.main_inference -h

1) U-Net_V1^(*)

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

2) U-Net_V2^(*)

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

3) U-Net_V2^(**)

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

4) U-Net_V2^(*+**)

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

5) U-Net_V2^{(*, JL)}

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

6) U-Net_V2^{(**+(*, JL))}

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

7) U-Net_V2^{(*+**+(*, JL))}

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

Extra : Final weights used for test

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

Docker

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

How to cite

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},
}

ryan0v0 / brats21 Goto Github PK

brats21's Introduction

A complete pipeline for BraTS 2021: RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021

Installation

1. Get the repository

2. Create a conda environment (recommended)

3. Install requirements

Data directory structure

Results of the paper

On validation

On test

Training

1) U-NetV1(*)

2) U-NetV2(*)

3) U-NetV2(**)

5) U-NetV2(*, JL)

Inference

1) U-NetV1(*)

2) U-NetV2(*)

3) U-NetV2(**)

4) U-NetV2(*+**)

5) U-NetV2(*, JL)

6) U-NetV2(**+(*, JL))

7) U-NetV2(*+**+(*, JL))

Extra : Final weights used for test

Docker

How to cite

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3) U-Net_V2^(**)

5) U-Net_V2^{(*, JL)}

1) U-Net_V1^(*)

2) U-Net_V2^(*)

3) U-Net_V2^(**)

4) U-Net_V2^(*+**)

5) U-Net_V2^{(*, JL)}

6) U-Net_V2^{(**+(*, JL))}

7) U-Net_V2^{(*+**+(*, JL))}