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Localizing Optic Disc and Cup for Glaucoma Screening via Deep Object Detection Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11039)

Abstract

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).

Keywords

Deep Object Glaucoma Screening ORIGA Dataset Cup-to-disc Ratio (CDR) Retinal Fundus Images 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Guangzhou Shiyuan Electronic Technology Company LimitedGuangzhouChina
  2. 2.Institute for Infocomm Research, A*STARSingaporeSingapore
  3. 3.South China University of TechnologyGuangzhouChina
  4. 4.Cixi Institute of Biomedical EngineeringChinese Academy of SciencesCixiChina

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