Deep level set learning for optic disc and cup segmentation

Pengshuai Yin, Yanwu Xu, Jinhui Zhu, Jiang Liu, Chang'an Yi, Huichou Huang, Qingyao Wu

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)


Optic disc and cup segmentation play an essential step towards automatic retinal diagnose system. The task is very challenging since the boundary between optic disc and cup is weak and the existing segmentation network with cross-entropy loss is hard to inject domain-specific knowledge. To solve the problem, we propose a level set based deep learning method for optic disc and cup segmentation. Particularly, we treat the output of the neural network as a level set and add several constraints to make the predicted level set satisfy some characteristics, such as the length constraint and region constraint. The length term lets the boundary tend to smooth while the region term lets the response inside the predicted area tend to be the same. The region term considers the relationship between pixels inside optic disc or cup while the cross-entropy loss treats the segmentation as a pixel-wise classification without considering the relationship between pixels. We conduct extensive experiments on several datasets including ORIGA and REFUGE and DRISHTI-GS dataset. The experiment results verify the effectiveness of our method.

Original languageEnglish
Pages (from-to)330-341
Number of pages12
Publication statusPublished - 13 Nov 2021
Externally publishedYes


  • Image segmentation
  • Medical image processing
  • Optic disc and cup segmentation

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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