Classification of Retinal Vessels into Artery-Vein in OCT Angiography Guided by Fundus Images

Jianyang Xie, Yonghuai Liu, Yalin Zheng, Pan Su, Yan Hu, Jianlong Yang, Jiang Liu, Yitian Zhao

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

1 Citation (Scopus)

Abstract

Automated classification of retinal artery (A) and vein (V) is of great importance for the management of eye diseases and systemic diseases. Traditional colour fundus images usually provide a large field of view of the retina in color, but often fail to capture the finer vessels and capillaries. In contrast, the new Optical Coherence Tomography Angiography (OCT-A) images can provide clear view of the retinal microvascular structure in gray scale down to capillary levels but cannot provide A/V information alone. For the first time, this study presents a new approach for the classification of A/V in OCT-A images, guided by the corresponding fundus images, so that the strengths of both modalities can be integrated together. To this end, we first estimate the vascular topologies of paired color fundus and OCT-A images respectively, then we propose a topological message passing algorithm to register the OCT-A onto color fundus images, and finally the integrated vascular topology map is categorized into arteries and veins by a clustering approach. The proposed method has been applied to a local dataset contains both fundus image and OCT-A, and it reliably identified individual arteries and veins in OCT-A. The experimental results show that despite lack of color and intensity information, it produces promising results. In addition, we will release our database to the public.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
EditorsAnne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages117-127
Number of pages11
ISBN (Print)9783030597245
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru
Duration: 4 Oct 20208 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12266 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/TerritoryPeru
CityLima
Period4/10/208/10/20

Keywords

  • Artery/vein classification
  • Message passing
  • OCT-A

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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