VAMPIRE project, School of Computing, University of Dundee, Dundee, United Kingdom.
Invest Ophthalmol Vis Sci. 2013 May 1;54(5):3546-59. doi: 10.1167/iovs.12-10347.
This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.
本文涉及自动视网膜图像分析 (ARIA) 算法的验证。由于篇幅和一致性的原因，我们专注于验证处理彩色眼底相机图像的算法，这是目前 ARIA 文献中最大的部分。我们概述了 ARIA 验证的背景（成像仪器和目标任务），总结了主要的图像分析和验证技术。然后，我们提出了一系列建议，重点是创建由国际联盟创建的大型测试数据存储库，这些存储库可通过经过审核的网站轻松访问，包括由多位专家针对特定临床任务进行的多中心注释，并且能够自动在存储的数据上运行提交的软件，同时具有明确且广泛达成共识的性能标准，以提供公平的比较。
