Current eye research · 2025
Quantitative Assessment of Choriocapillaris Blood Flow in Diabetic Retinopathy Using Optical Coherence Tomography Angiography.
СтатьяНазвание и аннотация: Перевод в очереди
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Fu Binzhe, Liu Jiajia, Wang Sheng, Feng Shuxian, Dai Yining, Liu Rong, Chen Wenliang, Li Chen Xi
Аннотация
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.
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.