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

被引：39

作者：

Wang, Jie ^{[1
,2
]}

Hormel, Tristan T. ^{[1
]}

You, Qisheng ^{[1
]}

Guo, Yukun ^{[1
]}

Wang, Xiaogang ^{[3
]}

Chen, Liu ^{[4
]}

Hwang, Thomas S. ^{[1
]}

Jia, Yali ^{[1
,2
]}

机构：

[1] Oregon Hlth & Sci Univ, Casey Eye Inst, Portland, OR 97239 USA

[2] Oregon Hlth & Sci Univ, Dept Biomed Engn, Portland, OR 97239 USA

[3] Shanxi Eye Hosp, Taiyuan, Shanxi, Peoples R China

[4] Oregon Hlth & Sci Univ, Dept Comp Sci & Elect Engn, Portland, OR 97239 USA

来源：

BIOMEDICAL OPTICS EXPRESS | 2020年 / 11卷 / 01期

基金：

中国国家自然科学基金; 美国国家卫生研究院;

关键词：

COHERENCE TOMOGRAPHY ANGIOGRAPHY; DIABETIC-RETINOPATHY; AUTOMATED QUANTIFICATION; CAPILLARY NONPERFUSION; ALGORITHM; ISCHEMIA;

D O I：

10.1364/BOE.11.000330

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

引用

页码：330 / 345

页数：16

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