CVI-Net-based automatic assessment of diabetic retinopathy severity using choroidal vascularity index in optical coherence tomography images.

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Abstract

A deep learning model called 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 non-proliferative 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 choroidal vascularity index (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. This article is protected by copyright. All rights reserved.This article is protected by copyright. All rights reserved.