Retinal vessel segmentation via deep learning network and fully-connected conditional random fields

Huazhu Fu, Yanwu Xu, Damon Wing Kee Wong, Jiang Liu

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

214 Citations (Scopus)

Abstract

Vessel segmentation is a key step for various medical applications. This paper introduces the deep learning architecture to improve the performance of retinal vessel segmentation. Deep learning architecture has been demonstrated having the powerful ability in automatically learning the rich hierarchical representations. In this paper, we formulate the vessel segmentation to a boundary detection problem, and utilize the fully convolutional neural networks (CNNs) to generate a vessel probability map. Our vessel probability map distinguishes the vessels and background in the inadequate contrast region, and has robustness to the pathological regions in the fundus image. Moreover, a fully-connected Conditional Random Fields (CRFs) is also employed to combine the discriminative vessel probability map and long-range interactions between pixels. Finally, a binary vessel segmentation result is obtained by our method. We show that our proposed method achieve a state-of-the-art vessel segmentation performance on the DRIVE and STARE datasets.

Original languageEnglish
Title of host publication2016 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
PublisherIEEE Computer Society
Pages698-701
Number of pages4
ISBN (Electronic)9781479923502
DOIs
Publication statusPublished - 15 Jun 2016
Externally publishedYes
Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
Duration: 13 Apr 201616 Apr 2016

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2016-June
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
Country/TerritoryCzech Republic
CityPrague
Period13/04/1616/04/16

Keywords

  • Conditional Random Fields
  • Convolutional Neural Networks
  • Vessel segmentation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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