Open AccessAutomated detection of photoreceptor disruption in mild diabetic retinopathy on volumetric optical coherence tomography
Author(s) -
Zhuo Wang,
Acner Camino,
Miao Zhang,
Jie Wang,
Thomas S. Hwang,
David J. Wilson,
David Huang,
Dengwang Li,
Yali Jia
Publication year - 2017
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.8.005384
Subject(s) - optical coherence tomography , diabetic retinopathy , artificial intelligence , cluster analysis , retina , computer science , population , medicine , tomography , coherence (philosophical gambling strategy) , ophthalmology , computer vision , pattern recognition (psychology) , mathematics , radiology , optics , diabetes mellitus , physics , statistics , environmental health , endocrinology
Diabetic retinopathy is a pathology where microvascular circulation abnormalities ultimately result in photoreceptor disruption and, consequently, permanent loss of vision. Here, we developed a method that automatically detects photoreceptor disruption in mild diabetic retinopathy by mapping ellipsoid zone reflectance abnormalities from en face optical coherence tomography images. The algorithm uses a fuzzy c-means scheme with a redefined membership function to assign a defect severity level on each pixel and generate a probability map of defect category affiliation. A novel scheme of unsupervised clustering optimization allows accurate detection of the affected area. The achieved accuracy, sensitivity and specificity were about 90% on a population of thirteen diseased subjects. This method shows potential for accurate and fast detection of early biomarkers in diabetic retinopathy evolution.