Open AccessAutomated detection of dilated capillaries on optical coherence tomography angiography
Author(s) -
Changlei Dongye,
Miao Zhang,
Thomas S. Hwang,
Jie Wang,
Simon S. Gao,
Liang Liu,
David Huang,
David J. Wilson,
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.8.001101
Subject(s) - optical coherence tomography , grading (engineering) , medicine , radiology , angiography , diabetic retinopathy , tomography , dilation (metric space) , artificial intelligence , computer science , diabetes mellitus , civil engineering , mathematics , combinatorics , engineering , endocrinology
Automated detection and grading of angiographic high-risk features in diabetic retinopathy can potentially enhance screening and clinical care. We have previously identified capillary dilation in angiograms of the deep plexus in optical coherence tomography angiography as a feature associated with severe diabetic retinopathy. In this study, we present an automated algorithm that uses hybrid contrast to distinguish angiograms with dilated capillaries from healthy controls and then applies saliency measurement to map the extent of the dilated capillary networks. The proposed algorithm agreed well with human grading.