Integrated Optic Disc and Cup Segmentation with Deep Learning

Glaucoma is a widespread ocular disorder leading to irreversible loss of vision. Therefore, there is a pressing need for cost-effective screening, such that preventive measures can be taken. This can be achieved with an accurate segmentation of the optic disc and cup from retinal images to obtain the cup-to-disc ratio. We describe a comprehensive solution based on applying convolutional neural networks to feature-exaggerated inputs emphasizing disc pallor without blood vessel obstruction, as well as the degree of vessel kinking. The produced raw probability maps then undergo a robust refinement procedure that takes into account prior knowledge about retinal structures. Analysis of these probability maps further allows us to obtain a confidence estimate on the correctness of the segmentation, which can be used to direct the most challenging cases for manual inspection. Tests on two large real-world databases, including the publicly-available MESSIDOR collection, demonstrate the effectiveness of our proposed system.