Department of Computer Science, Federal University of São Carlos, São Carlos, SP, Brazil.
Department of Ophthalmology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.
Sci Rep. 2021 Oct 6;11(1):19903. doi: 10.1038/s41598-021-99434-2.
Blood leakage from the vessels in the eye is the hallmark of many vascular eye diseases. One of the preclinical mouse models of retinal blood leakage, the very-low-density-lipoprotein receptor deficient mouse (Vldlr), is used for drug screening and mechanistic studies. Vessel leakage is usually examined using Fundus fluorescein angiography (FFA). However, interpreting FFA images of the Vldlr model is challenging as no automated and objective techniques exist for this model. A pipeline has been developed for quantifying leakage intensity and area including three tasks: (i) blood leakage identification, (ii) blood vessel segmentation, and (iii) image registration. Morphological operations followed by log-Gabor quadrature filters were used to identify leakage regions. In addition, a novel optic disk detection algorithm based on graph analysis was developed for registering the images at different timepoints. Blood leakage intensity and area measured by the methodology were compared to ground truth quantifications produced by two annotators. The relative difference between the quantifications from the method and those obtained from ground truth images was around 10% ± 6% for leakage intensity and 17% ± 8% for leakage region. The Pearson correlation coefficient between the method results and the ground truth was around 0.98 for leakage intensity and 0.94 for leakage region. Therefore, we presented a computational method for quantifying retinal vascular leakage and vessels using FFA in a preclinical angiogenesis model, the Vldlr model.
血管内的血液渗漏是许多血管性眼病的特征。非常低密度脂蛋白受体缺陷型小鼠(Vldlr)是一种用于药物筛选和机制研究的眼底荧光血管造影(FFA)视网膜血管渗漏的临床前小鼠模型。然而,由于缺乏用于该模型的自动化和客观技术,因此解释 Vldlr 模型的 FFA 图像具有挑战性。已经开发了一种用于量化渗漏强度和面积的流水线,包括三个任务:(i)血液渗漏识别,(ii)血管分割,和(iii)图像配准。使用形态操作和对数 Gabor 四元数滤波器来识别渗漏区域。此外,还开发了一种基于图分析的新型视盘检测算法,用于在不同时间点注册图像。通过该方法测量的血液渗漏强度和面积与由两名注释者生成的地面真实定量进行了比较。方法定量与地面真实图像定量之间的相对差异约为 10%±6%用于渗漏强度和 17%±8%用于渗漏区域。方法结果与地面真实之间的皮尔逊相关系数约为 0.98 用于渗漏强度和 0.94 用于渗漏区域。因此,我们提出了一种使用临床前血管生成模型(Vldlr 模型)FFA 量化视网膜血管渗漏和血管的计算方法。