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

OCT 血管造影中的动静脉差异分析用于糖尿病视网膜病变的客观分类

• 批准号: 10080731
• 负责人: Jennifer Irene Lim
• 金额: $ 35.17万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10080731
• 关键词: Adult; Affect; Angiography; Area; Arteries; Biological Markers; Blindness; Blood Vessels; Blood capillaries; Caliber; Classification; Clinical; Color; Complex; Derivation procedure; Detection; Diabetes Mellitus; Diabetic Retinopathy; Disease; Early Diagnosis; Evaluation; Exudate; Eye diseases; Fostering; Fundus photography; Geometry; Health Expenditures; Individual; Intervention; Investigation; Machine Learning; Maps; Methods; Microaneurysm; Modernization; Ophthalmologist; Optical Coherence Tomography; Optics; Performance; Process; Reflex action; Retina; Retinal Edemas; Retinal Hemorrhage; Sensitivity and Specificity; Source; Staging; Standardization; Symptoms; Techniques; Telemedicine; Testing; Thinness; Translating; Treatment outcome; Vascular Endothelial Growth Factors; Veins; Venous; Visual; Width; base; bevacizumab; clinical biomarkers; convolutional neural network; deep neural network; density; design; diabetic; diabetic patient; experience; fovea centralis; fundus imaging; global health; image registration; imaging capabilities; improved; indexing; instrument; macular edema; predictive marker; prevent; rural area; success; support vector machine; underserved area; vascular abnormality

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.

摘要：该项目旨在在光学相干断层扫描中建立差异动脉静脉分析 血管造影（八八），并验证糖尿病自动分类的综合八角 视网膜病（DR）。早期检测，及时干预以及对治疗结果的可靠评估是 防止DR的视觉丧失至关重要。众所周知，DR可以以不同的方式靶向动脉和静脉。 因此，差异动脉静脉分析可以更好地提供DR检测和分类的性能。 然而，临床八颗仪器缺乏动脉静脉分化的能力。在这个项目中，我们 提议使用OCT的定量特征分析，该分析与八章同时捕获，以引导动脉 八八静脉分化。第一个目的是在八角中建立自动动脉静脉分化。在 与我们最近展示的血管跟踪技术的协调，OCT强度/几何特征 将用于自动引导八颗动脉静脉分化。血液差异动脉分析 血管曲折（BVT），血管口径（BVC），血管密度（BVD），血管周边指数（VPI）， 血管分支系数（VBC），血管分支角（VBA），分支宽度比（BWR），Fovea Avascular 将实施区域区域（FAZ-A）和FAZ轮廓不规则性（FAZ-CI）。目标的关键成功标准 1个研究是证明八八的稳健动脉静脉分化，并为物镜建立八八特征 DR的检测和分类。第二个目的是验证DR的自动八八分类。我们 建议采用整体机器学习以整合多个分类器以实现强大的八 DR的分类。 AIM 2研究的关键成功标准是确定八八个特征和最佳功能 - 结合检测早期DR并建立八八个特征与临床之间的相关性 生物标志物。第三个目的是验证DR治疗的八八颗预测和评估。我们的初步八颗 研究具有抗血管内皮生长因子（抗VEGF）治疗的糖尿病性黄斑水肿（DME）的研究 表明BVD可以作为视觉改进的生物标志物预测。在此项目中，我们计划测试 用于DME治疗评估的差异动脉静脉分析。 AIM 3研究的关键成功标准是 识别动脉素食特征，以提供强大的预测和评估DME治疗结果。作为 替代方法，我们为深度机倾斜的完全卷积神经网络（FCNN）提出了 动脉静脉和DR分类。 FCNN中的早期层将产生简单的功能，这将被卷积 并过滤到更深的层中，以产生复杂的动脉静脉和DR分类。更远 调查通过机器学习过程学到的新功能之间的关系和 临床生物标志物将使我们能够优化设计以更好地分类。这个项目的成功将 铺平使用定量八达特征进行早期DR检测，客观预测和 评估治疗结果。