Optic Disc Segmentation in Fundus Images with Deep Learning Object Detector

Abstract

The Optic Disc (OD) is an important anatomical landmark in the fundus image to diagnose a myriad of diseases, such as glaucoma and Diabetic Retinopathy (DR) and to locate structures such as the macula and the main vascular arcade. However, locating and segmenting the OD are not easy tasks. Previous methods have employed a deep Convolutional Neural Network (CNN) without any need for hand-crafted features. Among these methods, RetinaNet has recently attracted attention as a simple one-stage object detector that performs quickly and efficiently while achieving state-of-the-art results. RetinaNet has proven its efficiency in multiple conventional object detection tasks with a larger training set that contains a sufficient number of diverse cases which are beyond reach in medical tasks. Thus, we propose an OD segmentation model from fundus images based on RetinaNet extension with DenseNet that addresses the vanishing gradient problem, enhances feature propagation, performs deep supervision, strengthens feature reuse and reduces the number of parameters. The experimental results using three publicly available databases show the efficacy of deep object detection network and the dense connectivity when applied to fundus images, which is a promising step in providing a segmentation to detect patients in the early stages of the disease.