https://github.com/hilab-git/aceloss

Implementations of "Learning Euler's Elastica Model for Medical Image Segmentation"
https://github.com/hilab-git/aceloss

active-contour-model loss-functions medical-image-segmentation

Last synced: 8 months ago
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Implementations of "Learning Euler's Elastica Model for Medical Image Segmentation"

• Host: GitHub
• URL: https://github.com/hilab-git/aceloss
• Owner: HiLab-git
• License: mit
• Created: 2020-11-01T13:33:36.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-01-10T12:53:50.000Z (over 5 years ago)
• Last Synced: 2025-10-14T14:40:01.839Z (8 months ago)
• Topics: active-contour-model, loss-functions, medical-image-segmentation
• Language: Python
• Homepage:
• Size: 2.49 MB
• Stars: 74
• Watchers: 2
• Forks: 12
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Active Contour Euler Elastica Loss Functions

Official implementations of paper: [Learning Euler's Elastica Model for Medical Image Segmentation](https://arxiv.org/pdf/2011.00526.pdf), and a short version was accepted by ISBI 2021 .

* 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

* ### **Pipeline of ACE loss**.

![](https://github.com/Luoxd1996/Active_Contour_Euler_Elastica_Loss/blob/main/ACELoss_pipeline.png) 

* ### **2D results and visualization**.

![](https://github.com/Luoxd1996/Active_Contour_Euler_Elastica_Loss/blob/main/table1.png) 

![](https://github.com/Luoxd1996/Active_Contour_Euler_Elastica_Loss/blob/main/figure1.png) 

* ### **3D results and visualization**.

![](https://github.com/Luoxd1996/Active_Contour_Euler_Elastica_Loss/blob/main/table2.png) 

![](https://github.com/Luoxd1996/Active_Contour_Euler_Elastica_Loss/blob/main/figure2.png) 

* 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][torch_link] version >= 0.4.1.

* Python  >= 3.6.

Follow official guidance to install. [Pytorch][torch_link].

[torch_link]:https://pytorch.org/

## 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:2011.00526},

	  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](https://github.com/xuuuuuuchen/Active-Contour-Loss)).

* Geodesic Active Contour Loss ([GAC](https://ieeexplore.ieee.org/document/9187860)).

* Elastic-Interaction-based Loss ([EILoss](https://github.com/charrywhite/elastic_interaction_based_loss))

## Acknowledgement

* We thank [Dr. Jun Ma](https://github.com/JunMa11) for instructive discussion of curvature implementation and also thank [Mr. Yechong Huang](https://github.com/huohuayuzhong) for instructive help during the implementation processing of 3D curvature, Sobel, and  Laplace operators.