MRBrainS18 challenge winning submission
Winning submission of the Grand Challenge on MR Brain Segmentation at MICCAI 2018 by team MISPL (Medical Image and Signal Processing Lab @ DGIST).
- Check the challenge results
- Download pretrained weights
- Check our article: 3D Patchwise U-Net with Transition Layers for MR Brain Segmentation
Usage
Create a python environment able to run the packages Numpy, TensorFlow and SimpleITK. Then you can execute the commands according to the required task as follows.
Training
bash run.sh <run number> train <GPU number> <checkpoint number>
bash run.sh 1 train 0 0
Testing
bash run.sh <run number> test <GPU number> <checkpoint number>
bash run.sh 1 test 0 0
Summaries
bash run.sh <run number> summaries <GPU number>
bash run.sh 1 summaries 0
Update global summary
bash get_summaries.sh
Check the summaries file
cat summary.txt
Citation
If you find the code useful for your research, please consider citing our article:
- MISPL_MRBrainS18:
@inproceedings{mispl_mrbrains18,
author={Luna, Miguel and Park, Sang Hyun},
title={3D Patchwise U-Net with Transition Layers for MR Brain Segmentation},
booktitle={Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries},
year={2019},
pages={394-403}
}