1. Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy
- Author
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Minhaj Alam, Bernadette A Miao, Jennifer I. Lim, Xincheng Yao, and David Le
- Subjects
0303 health sciences ,medicine.diagnostic_test ,business.industry ,Feature extraction ,Branching angle ,Diabetic retinopathy ,Optical coherence tomography angiography ,Vascular geometry ,medicine.disease ,01 natural sciences ,Atomic and Molecular Physics, and Optics ,Article ,010309 optics ,03 medical and health sciences ,Optical coherence tomography ,Fully automated ,0103 physical sciences ,Angiography ,medicine ,Nuclear medicine ,business ,030304 developmental biology ,Biotechnology ,Mathematics - Abstract
This study is to establish quantitative features of vascular geometry in optical coherence tomography angiography (OCTA) and validate them for the objective classification of diabetic retinopathy (DR). Six geometric features, including total vessel branching angle (VBA: θ), child branching angles (CBAs: α1 and α2), vessel branching coefficient (VBC), and children-to-parent vessel width ratios (VWR1 and VWR2), were automatically derived from each vessel branch in OCTA. Comparative analysis of heathy control, diabetes with no DR (NoDR), and non-proliferative DR (NPDR) was conducted. Our study reveals four quantitative OCTA features to produce robust DR detection and staging classification: (ANOVA, P
- Published
- 2019