Active Contour Euler Elastica Loss Functions
Official implementations of paper: Learning Euler's Elastica Model for Medical Image Segmentation.
- Implemented a novel active contour-based loss function, a combination of region term, length term, and elastica term (mean curvature).
- Reimplemented some popular active contour-based loss functions in different ways, such as 3D Active-Contour-Loss based on Sobel filter and max-and min-pool.
Introduction and Some Results
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Pipeline of ACE loss.
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2D results and visualization.
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3D results and visualization.
- If you want to use these methods just as constrains (combining 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 Based Loss Functions are useful in your research, please consider to cite:
@inproceedings{chen2020aceloss,
title={Learning Euler's Elastica Model for Medical Image Segmentation},
author={Chen, Xu and Luo, Xiangde and Zhao, Yitian and Zhang, Shaoting and Wang, Guotai and Zheng, Yalin},
journal={arXiv preprint arXiv arxiv.org/pdf/2011.00526.pdf},
year={2020}
}
@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).
- Geodesic Active Contour Loss (GAC).
- Elastic-Interaction-based Loss (EILoss)
Acknowledgement
- We thank Dr. Jun Ma for instructive discussion of curvature implementation and also thank Mr. Yechong Huang for instructive help during the implementation processing of 3D curvature, Sobel, and Laplace operators.