Optic Disc and Cup Segmentation Based on Enhanced SegNet

Due to imbalanced distributed and restricted medical resources, reliable analysis for medical images is hard to come by, and it is impractical to only rely on human beings to do all the analysis, which is time-consuming and not economic. Application of computer vision techniques in such fields emerges as the situation requires. In this paper, we use deep learning segmentation algorithm to segment the optic disc and the cup from each other and from the rest of the ophthalmoscopy photographs. For a better performance, we change the loss function and crop as a way of data augmentation. The segmentation results can be used to calculate the cup-to-disc ratio (CDR), which is further used to diagnose glaucoma. Challenges such as over-fitting, biased dataset, and poor generalization of the model exist in front of us. We illustrate our model and associated methods dealing with these challenges.