Differential artery-vein analysis in OCT angiography for objective classification of diabetic retinopathy

摘要：本项目旨在建立光学相干层析成像中的动-静脉差分分析。 血管造影术(OCTA)，并验证用于糖尿病自动分类的全面OCTA特征 视网膜病变(DR)。早期发现、及时干预和可靠的治疗结果评估是 这对于防止DR不可逆转的视力损失至关重要。众所周知，DR可以以不同的方式针对动脉和静脉。 因此，动-静脉差值分析可以提供更好的DR检测和分类性能。 然而，临床上的OCTA仪器缺乏区分动静脉的能力。在这个项目中，我们 建议使用与OCTA同时捕获的OCT的定量特征分析来指导动脉- OCTA中的静脉分化。第一个目标是在OCTA中建立自动的动-静脉区分。在……里面 与我们最近演示的血管跟踪技术、OCT强度/几何特征相协调 将用于自动指导OCTA的动-静脉分化。血液的动-静脉差示分析 血管曲度(BVT)、血管口径(BVC)、血管密度(BVD)、血管周长指数(VPI)、 血管分支系数(VBC)、血管分支角度(VBA)、分支宽度比(BWR)、无血管中心凹 将实施分区(FAZ-A)和FAZ轮廓不规则(FAZ-CI)。目标的关键成功标准 1项研究旨在证明OCTA的动-静脉分化较强，并建立OCTA的客观特征 Dr.的检测和分类第二个目标是验证Dr.We的OCTA自动分类 提出利用集成机器学习集成多个分类器来实现稳健的OCTA 目标2研究的关键成功标准是确定OCTA特征和最佳特征-- 联合检测早期DR，并建立OCTA特征与临床之间的相关性 生物标志物。第三个目标是验证OCTA对DR治疗的预测和评估。我们初步的OCTA 抗血管内皮生长因子治疗糖尿病黄斑水肿的研究 表明BVD可以作为预测视力改善的生物标志物。在这个项目中，我们计划测试 动脉-静脉差值分析用于二甲基醚治疗评估。目标3研究的关键成功标准是 确定动脉-静脉特征，为DME治疗结果提供可靠的预测和评估。作为一种 作为一种替代方法，我们提出了一种用于深度机器学习的完全卷积神经网络(FCNN 动-静脉和糖尿病视网膜病变分类。FCNN的早期层将产生简单的特征，这些特征将被卷积 并过滤到更深的层，产生用于动-静脉和DR分类的复杂特征。进一步 调查通过机器学习过程学习的新特征与 临床生物标记物将使我们能够优化设计，以更好地进行DR分类。这个项目的成功将会 为使用定量OCTA特征进行早期DR检测、客观预测和 对治疗结果的评估。

Abstract: This project aims to establish differential artery-vein analysis in optical coherence tomography angiography (OCTA), and to validate comprehensive OCTA features for automated classification of diabetic retinopathy (DR). Early detection, prompt intervention, and reliable assessment of treatment outcomes are essential to prevent irreversible visual loss from DR. It is known that DR can target arteries and veins differently. Therefore, differential artery-vein analysis can provide better performance of DR detection and classification. However, clinical OCTA instruments lack the capability of artery-vein differentiation. During this project, we propose to use quantitative feature analysis of OCT, which is concurrently captured with OCTA, to guide artery- vein differentiation in OCTA. The first aim is to establish automated artery-vein differentiation in OCTA. In coordination with our recently demonstrated blood vessel tracking technique, OCT intensity/geometry features will be used to guide artery-vein differentiation in OCTA automatically. Differential artery-vein analysis of blood vessel tortuosity (BVT), blood vessel caliber (BVC), blood vessel density (BVD), vessel perimeter index (VPI), vessel branching coefficient (VBC), vessel branching angle (VBA), branching width ratio (BWR), fovea avascular zone area (FAZ-A) and FAZ contour irregularity (FAZ-CI) will be implemented. Key success criterion of the aim 1 study is to demonstrate robust artery-vein differentiation in OCTA, and to establish OCTA features for objective detection and classification of DR. The second aim is to validate automated OCTA classification of DR. We propose to employ ensemble machine learning to integrate multiple classifiers to achieve robust OCTA classification of DR. Key success criterion of the aim 2 study is to identify OCTA features and optimal-feature- combination to detect early DR, and to establish the correlations between the OCTA features and clinical biomarkers. The third aim is to verify OCTA prediction and evaluation of DR treatment. Our preliminary OCTA study of diabetic macular edema (DME) with anti-vascular endothelial growth factor (anti-VEGF) treatment has shown that BVD can serve as a biomarker predictive of visual improvement. During this project, we plan to test differential artery-vein analysis for DME treatment evaluation. Key success criterion of the aim 3 study is to identify artery-vein features to provide robust prediction and evaluation of DME treatment outcomes. As an alternative approach, we propose a fully convolutional neural network (FCNN) for deep machine leaning based artery-vein and DR classification. Early layers in the FCNN will produce simple features, which will be convolved and filtered into deeper layers to produce complex features for artery-vein and DR classification. Further investigation of the relationship between the new features learned through the machine learning process and clinical biomarkers will allow us to optimize the design for better DR classification. Success of this project will pave the way towards using quantitative OCTA features for early DR detection, objective prediction and assessment of treatment outcomes.