SCH: Multimodal Retina Image Alignment and Applications

视网膜疾病的表征和治疗涉及流行病学和自然史研究，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，重要的是能够科学地分析和解释可以从视网膜上的不同区域甚至点获得的大量信息，并随着时间的推移评估视网膜结构和功能以响应治疗。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。任何一个人或阅读中心都越来越难以评估这些信息。该提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。

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.