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shape-attentive-unet

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

// repository documentation

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## 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:
<ol>
<li> Register on https://acdc.creatis.insa-lyon.fr/#challenges and download the ACDC - Segmentation dataset.</li>
<li> 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.</li>
<li> Run train.py using command python3 train.py </li>
</ol>
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}
}
```

shape-attentive-unet

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