Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography

Yukun Guo, Tristan T. Hormel, Honglian Xiong, Bingjie Wang, Acner Camino, Jie Wang, David Huang, Thomas Hwang, Jia Yali

Research output: Contribution to journalArticle

Abstract

The capillary nonperfusion area (NPA) is a key quantifiable biomarker in the evaluation of diabetic retinopathy (DR) using optical coherence tomography angiography (OCTA). However, signal reduction artifacts caused by vitreous floaters, pupil vignetting, or defocus present significant obstacles to accurate quantification. We have developed a convolutional neural network, MEDnet-V2, to distinguish NPA from signal reduction artifacts in 6×6 mm2 OCTA. The network achieves strong specificity and sensitivity for NPA detection across a wide range of DR severity and scan quality.

Original languageEnglish (US)
Pages (from-to)3257-3268
Number of pages12
JournalBiomedical Optics Express
Volume10
Issue number7
DOIs
StatePublished - Jul 1 2019

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angiography
Optical Coherence Tomography
Diabetic Retinopathy
Artifacts
learning
artifacts
Angiography
Learning
Pupil
tomography
vignetting
biomarkers
Biomarkers
pupils
Sensitivity and Specificity
evaluation
sensitivity

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

Cite this

Development and validation of a deep learning algorithm for distinguishing the nonperfusion area from signal reduction artifacts on OCT angiography. / Guo, Yukun; Hormel, Tristan T.; Xiong, Honglian; Wang, Bingjie; Camino, Acner; Wang, Jie; Huang, David; Hwang, Thomas; Yali, Jia.

In: Biomedical Optics Express, Vol. 10, No. 7, 01.07.2019, p. 3257-3268.

Research output: Contribution to journalArticle

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