Optic Disc Segmentation from Retinal Fundus Images via Deep Object Detection Networks

Xu Sun, Yanwu Xu, Wei Zhao, Tianyuan You, Jiang Liu

Research output: Journal PublicationArticlepeer-review

14 Citations (Scopus)

Abstract

Accurate optic disc (OD) segmentation is a fundamental step in computer-aided ocular disease diagnosis. In this paper, we propose a new pipeline to segment OD from retinal fundus images based on deep object detection networks. The fundus image segmentation problem is redefined as a relatively more straightforward object detection task. This then allows us to determine the OD boundary simply by transforming the predicted bounding box into a vertical and non-rotated ellipse. Using Faster R-CNN as the object detector, our method achieves state-of-the-art OD segmentation results on ORIGA dataset, outperforming existing methods in this field.

Original languageEnglish
Pages (from-to)5954-5957
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume2018
DOIs
Publication statusPublished - 1 Jul 2018
Externally publishedYes

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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