Affiliation:
1. Guangdong‐Hong Kong‐Macao Joint Laboratory for Intelligent Micro‐Nano Optoelectronic School of Physics and Optoelectronic Engineering, Foshan University Foshan China
2. Department of Ophthalmology Nanfang Hospital, Southern Medical University Guangzhou Guangdong China
Abstract
AbstractA deep learning model called choroidal vascularity index (CVI)‐Net is proposed to automatically segment the choroid layer and its vessels in overall optical coherence tomography (OCT) scans. Clinical parameters are then automatically quantified to determine structural and vascular changes in the choroid with the progression of diabetic retinopathy (DR) severity. The study includes 65 eyes consisting of 34 with proliferative DR (PDR), 17 with nonproliferative DR (NPDR), and 14 healthy controls from two OCT systems. On a dataset of 396 OCT B‐scan images with manually annotated ground truths, overall Dice coefficients of 96.6 ± 1.5 and 89.1 ± 3.1 are obtained by CVI‐Net for the choroid layer and vessel segmentation, respectively. The mean CVI values among the normal, NPDR, and PDR groups are consistent with reported outcomes. Statistical results indicate that CVI shows a significant negative correlation with DR severity level, and this correlation is independent of changes in other physiological parameters.
Funder
National Natural Science Foundation of China
Subject
General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry