Optic Disc Segmentation in Fundus Images with Deep Learning Object Detector

Manal Alghamdi

doi:10.3844/jcssp.2020.591.600

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.

Highlights

In fundus images, the Optic Disc (OD) is characterized by the bright, yellow and approximately elliptic area where the vessels are thick and dense (Sopharak et al, 2008)
The results show that the proposed model outperformed the state-of-the-art OD segmentation models in all three datasets
The existing methods based on traditional segmentation techniques such as morphological operations (Marin et al, 2015; Roychowdhury et al, 2016), Sliding Band Filters (SBF) Dashtbozorg et al (2015) and a variational model (Dai et al, 2017) achieved lower results than the other studies which are based on deep learning techniques

Summary

Introduction

The Optic Disc (OD) is characterized by the bright, yellow and approximately elliptic area where the vessels are thick and dense (Sopharak et al, 2008). An accurate OD localization and segmentation is an important step in diagnosing a variety of retinal diseases, such as glaucoma and optic disc pit and to check for any neovascularization at the optic disc as this is a manifestation of diabetic retinopathy (Jonas et al, 1988; Joshi et al, 2011). The majority of the recent studies have addressed this challenging task by using the traditional segmentation techniques. Machine learningbased approaches using Convolutional Neural Networks (CNNs) have been presented

Methods

Results

Discussion

Conclusion