Robust non-perfusion area detection in three retinal plexuses using convolutional neural network in OCT angiography

Jie Wang, Tristan T. Hormel, Qisheng You, Yukun Guo, Xiaogang Wang, Liu Chen, Thomas S. Hwang, Yali Jia

Research output: Contribution to journalArticle

Abstract

Non-perfusion area (NPA) is a quantitative biomarker useful for characterizing ischemia in diabetic retinopathy (DR). Projection-resolved optical coherence tomographic angiography (PR-OCTA) allows visualization of retinal capillaries and quantify NPA in individual plexuses. However, poor scan quality can make current NPA detection algorithms unreliable and inaccurate. In this work, we present a robust NPA detection algorithm using convolutional neural network (CNN). By merging information from OCT angiograms and OCT reflectance images, the CNN could exclude signal reduction and motion artifacts and detect the avascular features from local to global with the resolution preserved. Across a wide range of signal strength indices, and on both healthy and DR eyes, the algorithm achieved high accuracy and repeatability.

Original languageEnglish (US)
Pages (from-to)330-345
Number of pages16
JournalBiomedical Optics Express
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 1 2020

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ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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