Smith R Theodore, Lee Noah, Chen Jian, Busuioc Mihai, Laine Andrew F
Ophthalmology Department, Columbia University, New York, NY 10027 USA.
Conf Rec Asilomar Conf Signals Syst Comput. 2008 Oct 26;42:651-654. doi: 10.1109/ACSSC.2008.5074487.
The literature of the last three decades is replete with automatic methods for retinal image analysis. Acceptance has been limited due to post-processing or tuning requirements that may be just as time consuming as the original manual methods. The point of view herein is that by taking advantage of the human visual system and expert knowledge from the outset, the promised efficiencies of digital methods can be achieved in practice as well as in theory. Thus, simple labeling of regions of interest that is accepted and easily performed in a few moments by the human can provide enormous advantage to an already well-developed algorithm. Three examples are provided: drusen segmentation, image registration, and geographic atrophy segmentation, with applications to disease understanding.
过去三十年的文献中充斥着用于视网膜图像分析的自动方法。由于后处理或调整要求可能与原始手动方法一样耗时，其接受度受到限制。本文的观点是，从一开始就利用人类视觉系统和专家知识，数字方法所承诺的效率在实践和理论上都可以实现。因此，人类能够在几分钟内轻松接受并完成的感兴趣区域的简单标记，可为已经成熟的算法带来巨大优势。文中提供了三个例子：玻璃膜疣分割、图像配准和地图样萎缩分割，并将其应用于疾病理解。
