Optic Disc and Cup Segmentation for Glaucoma Characterization Using Deep Learning

Abstract

Glaucoma is one of the most common eye diseases that can cause irreversible vision loss due to damage to the optic nerve. Ophthalmologists consider a cup to optic disc ratio greater than 0.3 to be suggestive of glaucoma. Unfortunately, there is high variability among ophthalmologists in estimating the ratio since it is not easy to reliably measure optic disc and cup areas in a fundus image. Therefore, this paper proposes automatic methods to segment the optic disc and cup areas. There are two steps to estimate the ratio: region of interest (ROI) area detection (where optic disc is in the center) from a fundus image, followed by optic disc and cup segmentation. This paper focuses on automated methods to segment the optic disc and cup from the ROI. Fully convolutional networks (FCN) with U-Net architectures are used for the segmentation. The RIGA dataset (composed of three different fundus image datasets: MESSIDOR, Bin Rushed, and Magrabi), containing 750 fundus images, is used to train and test the FCNs. Our proposed FCNs show relatively better performance than other existing algorithms. The best segmentation results for optic disc show 0.95 Jaccard index, 0.98 F-measure, and 0.99 accuracy. The best segmentation results for cup show 0.80 Jaccard index, 0.88 F-measure, and 0.99 accuracy.