An Improved Disc Segmentation Based on U-Net Architecture for Glaucoma Diagnosis

Abstract

Various computer-aided diagnosis systems have been expanded and used for diagnosing glaucoma. Since the optic disc and optic cup are the main parameters for the early detection of glaucoma, this study proposes an accurate CAD system that firstly detects the optic disc and cup then classifies them into normal or abnormal. The U-Net architecture is employed. Despite its excellent segmentation performances, this model repeatedly extracts low-level features, which leads to redundant use of computational sources. To address these issues, a two-stage segmentation of the optic disc and cup was proposed. Firstly, a region of interest (ROI) is extracted from the fundus images by localizing and cutting the optic disc zone. Then, a U-Net model was built in order to obtain the refined segmentation. The public REFUGE dataset is adopted to validate proposed system. After a data augmentation step, an average accuracy of 0.97 and 0.96 for predicted OD cut off area and predicted original images respectively are obtained.