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基于自动和 ImageJ 阈值算法的糖尿病患者黄斑血管密度分析。

Automated and ImageJ thresholding algorithm-based analysis of macular vessel density in diabetic patients.

机构信息

Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India.

Department of Biostatistics, Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India.

出版信息

Indian J Ophthalmol. 2022 Jun;70(6):2050-2056. doi: 10.4103/ijo.IJO_74_22.

Abstract

PURPOSE

To assess the macular vessel density (VD) on optical coherence tomography angiography (OCT-A) using proprietary software (automated) and image processing software (manual) in diabetic patients.

METHODS

In a retrospective study, OCT-A images (Triton, TOPCON Inc.) of type 2 diabetics presenting to a tertiary eye care center in North India between January 2018 and December 2019 with or without nonproliferative diabetic retinopathy (NPDR) and with no macular edema were analyzed. Macular images of size 3 × 3 mm were binarized with global thresholding algorithms (ImageJ software). Outcome measures were superficial capillary plexus VD (SCP-VD, automated and manual), deep capillary plexus VD (DCP-VD, manual), and correlation between automated and manual SCP-VD.

RESULTS

OCT-A images of 89 eyes (55 patients) were analyzed: no diabetic retinopathy (NoDR): 29 eyes, mild NPDR: 29 eyes, and moderate NPDR: 31 eyes. Automated SCP-VD did not differ between NoDR and mild NPDR (P = 0.69), but differed between NoDR and moderate NPDR (P = 0.014) and between mild and moderate NPDR (P = 0.033). Manual SCP-VD (Huang and Otsu methods) did not differ between the groups. Manual DCP-VD differed between NoDR and mild NPDR and between NoDR and moderate NPDR, but not between mild and moderate NPDR with both Huang (P = 0.024, 0.003, and 0.51, respectively) and Otsu (P = 0.021, 0.006, and 0.43, respectively) methods. Automated SCP-VD correlated moderately with manual SCP-VD using Huang method (r = 0.51, P < 0.001) with a mean difference of -0.01% (agreement limits from -6.60% to +6.57%).

CONCLUSION

DCP-VD differs consistently between NoDR and NPDR with image processing, while SCP-VD shows variable results. Different thresholding algorithms provide different results, and there is a need to establish consensus on the most suited algorithm.

摘要

目的

使用专有的软件(自动)和图像处理软件(手动)评估光学相干断层血管造影(OCT-A)中的黄斑血管密度(VD)在糖尿病患者中的应用。

方法

在一项回顾性研究中,分析了 2018 年 1 月至 2019 年 12 月在印度北部一家三级眼科护理中心就诊的 2 型糖尿病患者的 OCT-A 图像(Triton,TOPCON Inc.),这些患者患有或不患有非增殖性糖尿病性视网膜病变(NPDR),且没有黄斑水肿。使用全局阈值算法(ImageJ 软件)对大小为 3×3mm 的黄斑图像进行二值化处理。主要观察指标为浅层毛细血管丛 VD(SCP-VD,自动和手动)、深层毛细血管丛 VD(DCP-VD,手动)以及自动和手动 SCP-VD 之间的相关性。

结果

分析了 89 只眼(55 例患者)的 OCT-A 图像:无糖尿病视网膜病变(NoDR):29 只眼,轻度 NPDR:29 只眼,中度 NPDR:31 只眼。自动 SCP-VD 在 NoDR 和轻度 NPDR 之间没有差异(P=0.69),但在 NoDR 和中度 NPDR 之间(P=0.014)以及在轻度和中度 NPDR 之间(P=0.033)存在差异。手动 SCP-VD(Huang 和 Otsu 方法)在各组之间没有差异。手动 DCP-VD 在 NoDR 和轻度 NPDR 以及 NoDR 和中度 NPDR 之间存在差异,但在轻度和中度 NPDR 之间,Huang(P=0.024,0.003 和 0.51)和 Otsu(P=0.021,0.006 和 0.43)方法之间没有差异。使用 Huang 方法,自动 SCP-VD 与手动 SCP-VD 中度相关(r=0.51,P<0.001),平均差异为-0.01%(一致性范围为-6.60%至+6.57%)。

结论

使用图像处理,DCP-VD 在 NoDR 和 NPDR 之间始终存在差异,而 SCP-VD 的结果则存在差异。不同的阈值算法提供不同的结果,需要就最合适的算法达成共识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13b5/9359289/933b3d3bd060/IJO-70-2050-g001.jpg

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