A Deep Learning Approach for Semantic Segmentation of Gonioscopic Images to Support Glaucoma Categorization

Abstract

We present a deep learning semantic segmentation algorithm for processing images acquired by a novel ophthalmic device, the NIDEK GS-1. The proposed model can sophisticate the current reference exam, called gonioscopy, for evaluating the risk of developing glaucoma, a severe eye pathology with a considerable worldwide impact in terms of costs and negative effects on affected people’s quality of life, and for inferring its categorization. The target eye region of gonioscopy is the interface between the iris and the cornea, and the anatomical structures that are located there. Our approach exploits a dense U-net architecture and is the first automatic system segmenting irido-corneal interface images from the novel device. Results show promising performance, providing about 88% of mean pixel-wise classification accuracy in a 5-fold cross-validation experiment on a very limited size dataset of annotated images.