Department of Computer Science, University of A Coruña, A Coruña, Spain.
CITIC-Research Center of Information and Communication Technologies, University of A Coruña, A Coruña, Spain.
PLoS One. 2019 Feb 22;14(2):e0212364. doi: 10.1371/journal.pone.0212364. eCollection 2019.
Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive retinal imaging modality of recent appearance that allows the visualization of the vascular structure at predefined depths based on the detection of the blood movement through the retinal vasculature. In this way, OCT-A images constitute a suitable scenario to analyze the retinal vascular properties of regions of interest as is the case of the macular area, measuring the characteristics of the foveal vascular and avascular zones. Extracted parameters of this region can be used as prognostic factors that determine if the patient suffers from certain pathologies (such as diabetic retinopathy or retinal vein occlusion, among others), indicating the associated pathological degree. The manual extraction of these biomedical parameters is a long, tedious and subjective process, introducing a significant intra and inter-expert variability, which penalizes the utility of the measurements. In addition, the absence of tools that automatically facilitate these calculations encourages the creation of computer-aided diagnosis frameworks that ease the doctor's work, increasing their productivity and making viable the use of this type of vascular biomarkers. In this work we propose a fully automatic system that identifies and precisely segments the region of the foveal avascular zone (FAZ) using a novel ophthalmological image modality as is OCT-A. The system combines different image processing techniques to firstly identify the region where the FAZ is contained and, secondly, proceed with the extraction of its precise contour. The system was validated using a representative set of 213 healthy and diabetic OCT-A images, providing accurate results with the best correlation with the manual measurements of two experts clinician of 0.93 as well as a Jaccard's index of 0.82 of the best experimental case in the experiments with healthy OCT-A images. The method also provided satisfactory results in diabetic OCT-A images, with a best correlation coefficient with the manual labeling of an expert clinician of 0.93 and a Jaccard's index of 0.83. This tool provides an accurate FAZ measurement with the desired objectivity and reproducibility, being very useful for the analysis of relevant vascular diseases through the study of the retinal micro-circulation.
光学相干断层扫描血管造影术（OCT-A）是一种最近出现的非侵入性视网膜成像方式，它允许根据视网膜血管中血液的运动检测，在预设深度可视化血管结构。通过这种方式，OCT-A 图像构成了分析感兴趣区域（如黄斑区域）视网膜血管特性的合适场景，测量黄斑区中心凹血管和无血管区的特征。该区域提取的参数可用作预测因子，以确定患者是否患有某些疾病（如糖尿病性视网膜病变或视网膜静脉阻塞等），并指示相关的病理程度。这些生物医学参数的手动提取是一个漫长、繁琐且主观的过程，引入了显著的专家内和专家间变异性，从而降低了测量的实用性。此外，缺乏自动方便这些计算的工具，鼓励创建计算机辅助诊断框架，减轻医生的工作负担，提高其工作效率，并使这种类型的血管生物标志物的使用成为可能。在这项工作中，我们提出了一种使用新型眼科图像模式（如 OCT-A）自动识别和精确分割中心凹无血管区（FAZ）的全自动系统。该系统结合了不同的图像处理技术，首先识别包含 FAZ 的区域，然后提取其精确轮廓。该系统使用一组有代表性的 213 张健康和糖尿病 OCT-A 图像进行了验证，为最佳实验案例在健康 OCT-A 图像的实验中，与两位临床专家的手动测量结果的相关性最好为 0.93，与手动测量结果的最佳 Jaccard 指数为 0.82，提供了准确的结果。该方法在糖尿病 OCT-A 图像中也取得了令人满意的结果，与一位临床专家的手动标记的最佳相关系数为 0.93，Jaccard 指数为 0.83。该工具提供了准确的 FAZ 测量，具有所需的客观性和可重复性，通过研究视网膜微循环，对相关血管疾病的分析非常有用。
