Code for our paper "SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation": https://arxiv.org/pdf/2001.07645v3.pdf. (MICCAI 2020)

Requirements

The library dependencies can be downloaded by running pip3 install -r requirements.txt.

Running the code

To run the code, you can follow the steps below:

1. Register on https://acdc.creatis.insa-lyon.fr/#challenges and download the ACDC - Segmentation dataset.
2. Assign the root directory of the dataset to the DATA_ROOT variable at the bottom of train.py. Alternatively, you can fill the flag -data-root to the root directory each time you run the code.
3. Run train.py using command python3 train.py

If you find our work helpful, please consider citing our work:

@misc{sun2020saunet,
    title={SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation},
    author={Jesse Sun and Fatemeh Darbehani and Mark Zaidi and Bo Wang},
    year={2020},
    eprint={2001.07645},
    archivePrefix={arXiv},
    primaryClass={eess.IV}
}