Automated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search

Pengxiao Zang, Jie Wang, Tristan T. Hormel, Liang Liu, David Huang, Yali Jia

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks – delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method’s segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.

Original languageEnglish (US)
Article number368487
Pages (from-to)4340-4352
Number of pages13
JournalBiomedical Optics Express
Volume10
Issue number8
DOIs
StatePublished - Aug 1 2019

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ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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