Code for Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

Overview

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.

Proposed Architecture

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 --num_classes 3 --epochs 350
  • Test : Run the test script on BraTS 2021 Training Dataset.
python test.py --num_classes 3

Acknowledgements

This repository makes liberal use of code from open_brats2020.

References

Citing our work

   @misc{peiris2022reciprocal,
      title={Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task}, 
      author={Himashi Peiris and Zhaolin Chen and Gary Egan and Mehrtash Harandi},
      year={2022},
      eprint={2201.03777},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
   }
    
Owner
Himashi Amanda Peiris
Former Senior Software Engineer at Pearson. Currently a PhD Candidate in Monash University
Himashi Amanda Peiris
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