Localizing Optic Disc And Cup For Glaucoma Screening Via Deep Object Detection Networks
COMPUTATIONAL PATHOLOGY AND OPHTHALMIC MEDICAL IMAGE ANALYSIS(2018)
摘要
Segmentation of the optic disc (OD) and optic cup (OC) from a retinal fundus image plays an important role for glaucoma screening and diagnosis. However, most existing methods only focus on pixel-level representations, and ignore the high level representations. In this work, we consider the high level concept, i.e., objectness constraint, for fundus structure analysis. Specifically, we introduce a deep object detection network to localize OD and OC simultaneously. The end-to-end architecture guarantees to learn more discriminative representations. Moreover, data from a similar domain can further contributes to our algorithm through transfer learning techniques. Experimental results show that our method achieves state-of-the-art OD and OC segmentation/localization results on ORIGA dataset. Moreover, the proposed method also obtains satisfactory glaucoma screening performance with the calculated vertical cup-to-disc ratio (CDR).
更多查看译文
关键词
Deep Object, Glaucoma Screening, ORIGA Dataset, Cup-to-disc Ratio (CDR), Retinal Fundus Images
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络