SCH: Multimodal Retina Image Alignment and Applications

SCH：多模态视网膜图像对齐和应用

• 批准号: 10491208
• 负责人: Cheolhong An
• 金额: $ 28.21万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491208
• 关键词: Area; Clinical Trials; Code; Computer software; Computers; Consult; Data; Data Collection; Data Set; Data Store; Database Management Systems; Databases; Digital Imaging and Communications in Medicine; Electronic Health Record; Ensure; Environmental Risk Factor; Epidemiology; Evaluation; Functional Imaging; Fundus photography; Genetic Risk; Goals; Guidelines; Health; Image; Image Analysis; Internet; Intuition; Link; Masks; Medical; Methods; Multimodal Imaging; Natural History; Ophthalmologist; Patients; Perimetry; Persons; Preparation; Protocols documentation; Reading; Resolution; Retina; Retinal Diseases; Safety; Secure; Structure; System; Technology; Testing; Time; Training; Work; base; data exchange; data management; deep learning; disease diagnosis; encryption; fundus imaging; image processing; imaging platform; instrument; multimodality; optical imaging; retinal imaging; sample fixation; treatment response

Progress is the characterization and treatment of retinal disease involves epidemiological and natural history studies which include genetic and environmental risk actor evaluation as well as clinical trials. As treatments advance, it is important to be able to scientifically analyze and interpret a large amount of information that can be procured from different areas and even points on the retina and evaluate retinal structure and function over time in response to therapies. Currently there is a proliferation of technology to provide data and this data comes from many instruments and different companies. It is increasingly difficult for any one person or reading center to evaluate this information. The goal of this proposal is to develop deep-learning based multimodal retinal image processing methods to help the ophthalmologist to quickly detect and diagnose disease.

进展是视网膜疾病的特征和治疗涉及流行病学和自然史研究，其中包括遗传和环境风险因素评估以及临床试验。随着治疗的进步，能够科学地分析和解释从视网膜上不同区域甚至不同点获得的大量信息，并随着时间的推移评估视网膜结构和功能对治疗的响应是重要的。目前，提供数据的技术激增，这些数据来自许多仪器和不同的公司。对于任何一个人或阅读中心来说，评估这些信息都越来越困难。该方案的目标是开发基于深度学习的多模式视网膜图像处理方法，以帮助眼科医生快速检测和诊断疾病。