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Abstract #82648 Published in IGR 20-4
Joint optic disc and cup segmentation using semi-supervised conditional GANs
Liu S; Hong J; Lu X; Jia X; Lin Z; Zhou Y; Liu Y; Zhang HComputers in Biology and Medicine 2019; 115: 103485
Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc and cup from fundus images. However, most existing techniques fail to obtain satisfactory segmentation performance because a significant number of pixel-level annotated data are often unavailable during training. To cope with this limitation, in this paper, we propose an effective joint optic disc and cup segmentation method based on semi-supervised conditional Generative Adversarial Nets (GANs). Our architecture consists of a segmentation net, a generator and a discriminator, to learn a mapping between the fundus images and the corresponding segmentation maps. Additionally, we employ both labeled and unlabeled data to improve the segmentation performance. The extensive experiments show that our method achieves state-of-the-art optic disc and cup segmentation results on both ORIGA and REFUGE datasets.
Department of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, 510006, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China.
Full articleClassification:
2.14 Optic disc (Part of: 2 Anatomical structures in glaucoma)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
6.30 Other (Part of: 6 Clinical examination methods)
