Optic disc and fovea detection using multi-stage region-based convolutional neural network

Xuechen Li, Linlin Shen, Jiang Duan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Detection of the optic disc (OD) and fovea in retinal images is an important step for automated detection of retinal disease in digital color photographs of the retina. Together with the vasculature, the optic disc and the fovea are the most important anatomical landmarks on the posterior pole of the retina. In this work, we presented a multi-stage region-based convolutional neural network for optic disc and fovea detection. In the first stage, standard faster-RCNN and SVM were employed for OD segmentation. In the second stage, the relative position information (RPI)-based faster-RCNN was proposed for fovea detection. The experimental result showed the average Euclidean distance with ground truth were 32.6 and 52 pixels for OD and fovea, respectively. The average Jaccard and dice index for OD segmentation were 0.8018 and 0.8873, respectively. The RPI-based faster-RCNN outperformed the standard network.

Original languageEnglish
Title of host publicationISICDM 2018 - Proceedings of the 2nd International Symposium on Image Computing and Digital Medicine
PublisherAssociation for Computing Machinery
Pages7-11
Number of pages5
ISBN (Electronic)9781450365338
DOIs
Publication statusPublished - 13 Oct 2018
Externally publishedYes
Event2nd International Symposium on Image Computing and Digital Medicine, ISICDM 2018 - Chengdu, China
Duration: 13 Oct 201815 Oct 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2nd International Symposium on Image Computing and Digital Medicine, ISICDM 2018
Country/TerritoryChina
CityChengdu
Period13/10/1815/10/18

Keywords

  • Deep learning
  • Faster-RCNN
  • Fovea detection
  • Fundus image
  • Optic disc

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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