himashi92 / vizviva_brats_2021

[Brainlesion 2021] Official PyTorch Implementation for Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

BRATS 2021 Solution For Segmentation Task

This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task in ArXiv and in Springer Nature.

Environment

Prepare an environment with python=3.8, and then run the command "pip install -r requirements.txt" for the dependencies.

Data Preparation

• File structure

   BRATS2021
    |---Data
    |   |--- RSNA_ASNR_MICCAI_BraTS2021_TrainingData
    |   |   |--- BraTS2021_00000
    |   |   |   |--- BraTS2021_00000_flair...
    |   
    |              
    |   
    |
    |---train.py
    |---test.py
    ...
  

Train/Test

• Train : Run the train script on BraTS 2021 Training Dataset with Base model Configurations.

python train.py --epochs 350

• Test : Run the test script on BraTS 2021 Training Dataset.

python test.py

Pre-trained Model

https://drive.google.com/file/d/11YmBPePPmnqE9W40ZqschovmiPx6lZ-2/view?usp=sharing

Acknowledgements

This repository makes liberal use of code from open_brats2020.

References

• BraTS 2021

Citing our work

    @inproceedings{peiris2022reciprocal,
      title={Reciprocal adversarial learning for brain tumor segmentation: a solution to BraTS challenge 2021 segmentation task},
      author={Peiris, Himashi and Chen, Zhaolin and Egan, Gary and Harandi, Mehrtash},
      booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I},
      pages={171--181},
      year={2022},
      organization={Springer}
    }