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.
Prepare an environment with python=3.8, and then run the command "pip install -r requirements.txt" for the dependencies.
BRATS2021
|---Data
| |--- RSNA_ASNR_MICCAI_BraTS2021_TrainingData
| | |--- BraTS2021_00000
| | | |--- BraTS2021_00000_flair...
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|
|
|
|---train.py
|---test.py
...
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
https://drive.google.com/file/d/11YmBPePPmnqE9W40ZqschovmiPx6lZ-2/view?usp=sharing
This repository makes liberal use of code from open_brats2020.
@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}
}