GDCSeg-Net: general optic disc and cup segmentation network for multi-device fundus images.

Qianlong Zhu,Zuoyong Li,Meng Wang,Fei Shi,Qingquan Meng,Jiahuan Song,Dehui Xiang,Lingjiao Pan,Gaohui Luo,Zhongyue Chen,Xinjian Chen,Weifang Zhu

doi:10.1364/boe.434841

Abstract

Accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is crucial for the analysis of many retinal diseases, such as the screening and diagnosis of glaucoma and atrophy segmentation. Due to domain shift between different datasets caused by different acquisition devices and modes and inadequate training caused by small sample dataset, the existing deep-learning-based OD and OC segmentation networks have poor generalization ability for different fundus image datasets. In this paper, adopting the mixed training strategy based on different datasets for the first time, we propose an encoder-decoder based general OD and OC segmentation network (named as GDCSeg-Net) with the newly designed multi-scale weight-shared attention (MSA) module and densely connected depthwise separable convolution (DSC) module, to effectively overcome these two problems. Experimental results show that our proposed GDCSeg-Net is competitive with other state-of-the-art methods on five different public fundus image datasets, including REFUGE, MESSIDOR, RIM-ONE-R3, Drishti-GS and IDRiD.

Highlights

Optic disc (OD) and optic cup (OC), retinal vessel and macula are three most salient features in retinal fundus
In this paper, based on the mixed training strategy of different datasets, an encoderdecoder structure based general OD and OC segmentation network is proposed, which can effectively overcome the problems of the appearance differences caused by different acquisition devices and inadequate training caused by small sample dataset
-A novel densely connected depthwise separable convolution (DSC) module is proposed and embedded as the output layer of the GDCSeg-Net, which fully fuses the multi-scale features extracted by depthwise separable convolution layer-by-layer via dense connections and leads the network to efficiently focus on the targets

Summary

Introduction

Optic disc (OD) and optic cup (OC), retinal vessel and macula are three most salient features in retinal fundus. The accurate segmentation of OD and OC in fundus images (as shown in Fig. 1) is crucial for the analysis of many retinal diseases, e.g., OD and OC segmentation based optic cup-to-disc ratio is one of the main criteria for clinical screening and diagnosis of glaucoma [1]. REFUGE and MESSIDOR datasets both contain 1200 fundus images, while Drishti-GS, RIM-ONE-R3 and IDRID only contain 101, 159 and 81 ones respectively. Due to the insufficient training, the small sample training based segmentation network usually has bad segmentation performance and generality

Methods

Results

Conclusion