Deep level set learning for optic disc and cup segmentation

Pengshuai Yin

doi:10.1016/j.neucom.2021.08.102

Deep level set learning for optic disc and cup segmentation

2021-11-01

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DOI：10.1016/j.neucom.2021.08.102

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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.

Keywords:

Optic disc and cup segmentation

Image segmentation

Medical image processing

Deep level set learning for optic disc and cup segmentation

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