Quantitative Assessment of Choriocapillaris Blood Flow in Diabetic Retinopathy Using Optical Coherence Tomography Angiography.

IF 2 4区医学 Q3 OPHTHALMOLOGY

Current Eye Research Pub Date : 2025-11-01 Epub Date: 2025-07-24 DOI:10.1080/02713683.2025.2537284

Binzhe Fu, Jiajia Liu, Sheng Wang, Shuxian Feng, Yining Dai, Rong Liu, Wenliang Chen, Chen Xi Li

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引用次数: 0

Abstract

Purpose: To develop an automated method for segmenting and quantifying the choriocapillaris (CC) layer using swept-source optical coherence tomography angiography (SS-OCTA), aimed at evaluating CC perfusion changes in diabetic retinopathy (DR) patients and facilitating clinical research.

Methods: We proposed a traditional image processing algorithm combining shadow compensation and intensity gradients to segment the CC layer in eyes at various stages of DR. The algorithm was refined for artifact removal in CC blood flow analysis. It was tested on 25 manually segmented cases including normal eyes, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). CC blood flow quantification was performed on 69 subjects.

Results: The segmentation algorithm showed high accuracy, with a maximum mean positional error of 4.086 ± 4.304 μm for Bruch's membrane (BM) and a minimum average DICE coefficient of 0.831 for CC segmentation. The CC flow deficit percentage (FD%) for normal eyes, NPDR eyes, and PDR eyes were 9.79 ± 2.29%, 12.25 ± 3.89%, and 15.35 ± 4.00%, respectively, with significant differences between groups (p < 0.05).

Conclusions: The automated CC segmentation and quantification algorithm developed in this study provides an accurate and reliable method for assessing CC in DR patients. This method has potential for widespread clinical application in evaluating CC perfusion changes across various stages of DR.

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使用光学相干断层扫描血管造影定量评估糖尿病视网膜病变的绒毛膜毛细血管血流。

目的：建立一种基于扫描源光学相干断层扫描血管造影（SS-OCTA）的绒毛膜毛细血管（CC）层自动分割和定量的方法，旨在评估糖尿病视网膜病变（DR）患者的CC灌注变化，为临床研究提供依据。方法：提出了一种结合阴影补偿和强度梯度的传统图像处理算法，对dr各阶段眼睛CC层进行分割，并对算法进行了改进，用于CC血流分析中的伪影去除。对25例人工分割的正常眼、非增殖性糖尿病视网膜病变（NPDR）和增殖性糖尿病视网膜病变（PDR）进行了测试。对69例受试者进行CC血流定量。结果：该分割算法具有较高的分割精度，布鲁赫膜（BM）分割的最大平均位置误差为4.086±4.304 μm， CC分割的最小平均DICE系数为0.831。正常眼、NPDR眼和PDR眼CC血流亏缺率（FD%）分别为9.79±2.29%、12.25±3.89%和15.35±4.00%，组间差异有统计学意义(p)。结论：本研究建立的CC自动分割与量化算法为评估DR患者CC提供了一种准确可靠的方法。该方法具有广泛的临床应用潜力，可用于评估DR不同阶段的CC灌注变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Eye Research 医学-眼科学

CiteScore

4.60

自引率

0.00%

发文量

163

审稿时长

12 months

期刊介绍： The principal aim of Current Eye Research is to provide rapid publication of full papers, short communications and mini-reviews, all high quality. Current Eye Research publishes articles encompassing all the areas of eye research. Subject areas include the following: clinical research, anatomy, physiology, biophysics, biochemistry, pharmacology, developmental biology, microbiology and immunology.