Objective Diagnosis & Quantification of Retinal Disease

描述（由申请人提供）：目前视网膜疾病（如年龄相关性黄斑变性）的诊断和定量依赖于受过培训的专家对彩色视网膜图像和荧光素血管造影片的主观解释。诊断过程的主观性使临床试验结果在临床实践中的适当应用变得复杂。随着视网膜疾病基因分型的快速进展和在分子水平上靶向疾病的治疗的发展，对视网膜疾病的准确、可重复和精确的表型分型的需求变得至关重要。高分辨率数字眼底成像系统、高等数学和高速计算的发展现在已经使视网膜病变的完全量化成为可能。为了解决这个重要的问题，一个由视网膜图像解释专家、数学家、计算机科学家和图像处理专家组成的团队已经成立。视网膜图像分析和量化的过程分为三个主要组成部分：（1）图像选择和准备，（2）图像对准或配准，以及（3）图像分割，识别和量化。在本提案的第一阶段（R21），目标是开发一个原型系统，以回答与这三个组成部分中的每一个有关的重要可行性问题。实现这一目标将需要开发图像质量评估指标，阴影校正算法，全球扭曲功能，以及各种视网膜结构的强度分布数据库。在本提案的第二阶段（R33），将根据现有的金标准（专家人工判读）和其他客观和主观临床参数对R21阶段的原型系统进行完善、优化和验证。在这个拟议的研究项目结束时，将开发一种经过验证的技术，用于视网膜疾病的可重复，准确和客观的诊断，分类和量化。该技术将在未来的临床和临床前研究以及日常临床实践中具有重要价值。

DESCRIPTION (provided by applicant): The current diagnosis and quantification of retinal diseases, such as age-related macular degeneration, relies on the subjective interpretation of color retinal images and fluorescein angiograms by trained experts. The subjectivity of the diagnostic process has complicated the appropriate application of clinical trial results to clinical practice. With rapid advances in the genotyping of retinal disease and the development of therapies which target disease at the molecular level, the need for accurate, reproducible, and precise phenotyping of retinal disease has become critical. The development of high-resolution digital fundus imaging systems, advanced mathematics, and high-speed computing has now made complete quantification of retinal lesions feasible. To solve this important problem, a team of retinal image interpretation experts, mathematicians, computer scientists, and image processing experts has been assembled. The process of retinal image analysis and quantification is divided into three major components: (1) image selection and preparation, (2) image alignment or registration, and (3) image segmentation, identification, and quantification. In the first (R21) phase of this proposal, the aim is to develop a prototype system to answer important feasibility questions related to each of these three components. Accomplishing this aim will require the development of image quality assessment metrics, shade-correction algorithms, global warping functions, and a database of intensity profiles for various retinal structures. In the second (R33) phase of this proposal, the prototype system from the R21 phase will be refined, optimized, and validated against the existing gold standard (expert human interpretation) and other objective and subjective clinical parameters. At the end of this proposed research project, a validated technology will have been developed for the reproducible, accurate, and objective diagnosis, classification, and quantification of retinal disease. This technology will be of great value in future clinical and pre-clinical studies, and in everyday clinical practice.