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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.