Deep level set learning for optic disc and cup segmentation

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. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study introduces a level set based deep learning method for optic disc and cup segmentation, addressing the challenge of injecting domain-specific knowledge into existing segmentation networks. By adding constraints and considering pixel relationships, the proposed method effectively solves the problem. Experimental results confirm the effectiveness of the approach.