Automated Image-based Biomarker Computation Tools for Diabetic Retinopathy

描述（由申请人提供）：在这个STTR项目中，我们展示了EyeMark，一套先进的图像分析工具，用于使用视网膜眼底图像自动计算糖尿病视网膜病变（DR）的生物标志物。具体来说，我们将开发用于计算微动脉瘤（MA）出现和消失率（共同称为周转率）的工具，作为量化DR进展风险的生物标志物。可靠的基于图像的生物标志物的可用性将对DR护理的各个方面产生高度积极的影响，包括筛查、监测进展、药物发现和临床研究。测量MA周转率涉及两个劳动密集型步骤：仔细校准当前和基线图像，以及标记单个MA。如果完全由人工评分，这个过程非常耗时，而且容易出错。这个项目的主要目标是通过自动化MA周转率测量所涉及的两个步骤来克服这些限制：准确的图像配准和MA检测。在第一阶段，我们设计并开发了一个MA周转率计算原型工具，该工具可以稳健地注册纵向图像（即使有多个病变改变）并有效检测MAs（病变水平AUROC=0.92）。该工具通过将MA周转率报告为一个范围，为混淆图像因素提供了优雅的退化，从而捕获了MA检测的固有信心。在II期结束时，我们将开发一种经过临床验证的端到端桌面软件，用于强大的、自动计算MA周转生物标志物，可以在云上工作，在几乎恒定的时间内产生结果（对于大型数据集），并且还为临床医生提供直观的可视化工具，以更有效地监测DR进展。

DESCRIPTION (provided by applicant): In this STTR project, we present EyeMark, a set of advanced image analysis tools for automated computation of biomarkers for diabetic retinopathy (DR) using retinal fundus images. Specifically, we will develop tools for computation of microaneurysm (MA) appearance and disappearance rates (jointly known as turnover rates) for use as a biomarker in quantifying DR progression risk. The availability of a reliable image-based biomarker will have high positive influence on various aspects of DR care, including screening, monitoring progression, drug discovery and clinical research. Measuring MA turnover involves two labor intensive steps: careful alignment of current and baseline images, and marking of individual MAs. This process is very time consuming and prone to error, if done entirely by human graders. The primary goal of this project is to overcome these limitations by automating both the steps involved in MA turnover measurement: accurate image registration, and MA detection. In Phase I we have designed and developed a MA turnover computation prototype tool that robustly registers longitudinal images (even with multiple lesion changes) and effectively detects MAs (lesion level AUROC=0.92). The tool provides graceful degradation to confounding image factors by reporting MA turnover as a range, thereby capturing the inherent confidence in MA detection. By the end of Phase II we will develop a clinically validated end-to-end desktop software for robust, automated computation of MA turnover biomarker, that can work on the cloud to produces results in near constant time (for large datasets), and also provide intuitive visualization tools for clinicians to more effectively monitor DR progression.