Active Contour Euler Elastica Loss Functions

Official implementations of Learning Euler's Elastica Model for Medical Image Segmentation

• A novel active contour based loss function.

Introduction

• Notes: More details will be released latter, the arXiv version of this paper will be available soon.
• If you want to use these methods just as constrains (combine with dice loss or ce loss), you can use torch.mean() to replace torch.sum().

Requirements

Some important required packages include:

• Pytorch version >=0.4.1.
• Python == 3.6 Follow official guidance to install Pytorch.

Citation

If you find Active-Contour-Loss is useful in your research, please consider to cite:

@inproceedings{chen2019learning,
  title={Learning Active Contour Models for Medical Image Segmentation},
  author={Chen, Xu and Williams, Bryan M and Vallabhaneni, Srinivasa R and Czanner, Gabriela and Williams, Rachel and Zheng, Yalin},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={11632--11640},
  year={2019}
}

Other Active Contour Based Loss Functions

• Active Contour Loss (ACLoss)