Disentangled Representations for Domain-generalized Cardiac Segmentation [Paper]. In M&Ms Challenge of STACOM 2020.
The repository is created by Xiao Liu, Spyridon Thermos, Agisilaos Chartsias, Alison O'Neil, and Sotirios A. Tsaftaris, as a result of the collaboration between The University of Edinburgh and Canon Medical Systems Europe.
This repository contains the official PyTorch implementation of the Resolution Augmentation (RA) and Factor-based Augmentation (FA) methods proposed in the paper.
- Pytorch 1.5.1 or higher with GPU support
- Python 3.7.2 or higher
- SciPy 1.5.2 or higher
- tqdm
- logging
- CUDA toolkit 10 or newer
In this repository, we train a SDNet [code], [paper] with our proposed Resolution Augmentation and Factor-based Augmentation in a semi-supervised manner.
We propose to use random resampling to augment the original dataset such that the resolutions of all the data are equally distributed in a certain range.
We first pre-train a SDNet model to extract the anatomy and modality factors. Then mix the anatomy and modality factors to generate new images.
To train the model, run the following command:
python train.py -e 50 -bs 4 -g 0
If you find our method useful please cite the following paper:
@article{liu2020disentangled,
title={Disentangled Representations for Domain-generalized Cardiac Segmentation},
author={Liu, Xiao and Thermos, Spyridon and Chartsias, Agisilaos and O'Neil, Alison and Tsaftaris, Sotirios A},
journal={arXiv preprint arXiv:2008.11514},
year={2020}
}
All scripts are released under the MIT License.