Open AccessRobust non-perfusion area detection in three retinal plexuses using convolutional neural network in OCT angiography
Author(s) -
Jie Wang,
Tristan T. Hormel,
Qi Sheng You,
Yukun Guo,
Xiaogang Wang,
Liu Chen,
Thomas S. Hwang,
Yali Jia
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.11.000330
Subject(s) - convolutional neural network , computer science , artificial intelligence , optical coherence tomography , projection (relational algebra) , retinal , computer vision , angiography , image quality , pattern recognition (psychology) , medicine , ophthalmology , algorithm , radiology , image (mathematics)
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.