MyoPS 2020: Fully Automated Deep Learning-based Segmentation of Normal, Infarcted and Edema Regions from Multiple Cardiac MRI Sequences
@inproceedings{zhang2020fully,
title={Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences},
author={Zhang, Xiaoran and Noga, Michelle and Punithakumar, Kumaradevan},
booktitle={Myocardial Pathology Segmentation Combining Multi-Sequence CMR Challenge},
pages={82--91},
year={2020},
organization={Springer}
}
Please refer to challenge website [link] for dataset access. The dataset contains three folders: train25, train25_myops_gd, test20.
- UNet:
conda env create -f myops_unet.yml
- Mask-RCNN and UNet++:
conda env create -f myops_mrcnn_unetpp.yml
├── Data
| ├── Original_data # Place the downloaded dataset here
| | ├── train25
| | ├── train25_myops_gd
| | ├── test20
├── mask_rcnn_coco.h5 # Downloaded pre-trained mask_rcnn weights
├── config.py
├── data_creator.py
├── ...
- Data creator including random warping augmentation
python data_creator.py
-
Train networks
- Train UNet for LV_BP, RV_BP, LV_NM, LV_ME, LV_MS blocks
python train_UNet.py
- Train Mask-RCNN for LV_ME and LV_MS blocks
- Download pretrained mask_rcnn_coco.h5 at [here] and place it in the current folder.
- Train Mask-RCNN for LV_ME block
python train_MaskRCNN.py --mode 'LV_ME'
- Train Mask-RCNN for LV_MS block
python train_MaskRCNN.py --mode 'LV_MS'
- Train UNet++ for LV_ME and LV_MS blocks
python train_UNetplusplus.py
-
Test networks:
- Test UNet
python test_UNet.py
- Test Mask-RCNN
- Test LV_ME block
python test_MaskRCNN.py --mode 'LV_ME'
- Test LV_MS block
python test_MaskRCNN.py --mode 'LV_MS'
- Test UNet++
python test_UNetplusplus.py
-
Post-processing and linear decoder:
python post_processing.py
- Please cite the official Mask-RCNN and UNet++ implementations if you use them:
- The authors would wish to acknowledge Compute Canada for providing the computation resource.