Ellipse Detection of Optic Disc-and-Cup Boundary in Fundus Images

Abstract

Glaucoma is an eye disease that damages the optic nerve and leads to loss of vision. The diagnosis of glaucoma involves measurement of cup-to-disc ratio from retinal fundus images, which necessitates the detection of the optic disc-and-cup boundary as a crucial task for glaucoma screening. Most existing computer-aided diagnosis (CAD) systems focus on the segmentation approaches but ignore the localization approaches, which requires less human annotation cost. In this paper, we propose a deep learning-based framework to jointly localize the ellipse for the optic disc (OD) and optic cup (OC) regions. Instead of detecting a bounding box like in most object detection approaches, we directly estimate the parameters of an ellipse that suffices to capture the morphology of each OD and OC region for calculating the cup-to-disc ratio. We use two modules to detect the ellipses for OD and OC regions, where the OD region serves as attention to the OC region. The proposed framework achieves competitive results against the state-of-the-art segmentation methods with less supervision. We empirically evaluate our framework with the recent state-of-the-art segmentation models on two scenarios where the training data and test data come from the same and different domains.