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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. [ABSTRACT FROM AUTHOR]

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Most of the existing automatic screening methods first segment the main structure and subsequently calculate the clinical measurement for the detection and screening of glaucoma. However, these measurement-based methods rely heavily on the segmentation accuracy and ignore various visual features. In this paper, we introduce a deep learning technique to gain additional image-relevant information and screen glaucoma from the fundus image directly. Specifically, a novel disc-aware ensemble network for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region. Four deep streams on different levels and modules are, respectively, considered as global image stream, segmentation-guided network, local disc region stream, and disc polar transformation stream. Finally, the output probabilities of different streams are fused as the final screening result. The experiments on two glaucoma data sets (SCES and new SINDI data sets) show that our method outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]