年龄相关性黄斑变性(AMD)和Stargardt病(STGD)的交互式图像分析。

R Theodore Smith, Noah Lee, Jian Chen, Mihai Busuioc, Andrew F Laine
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引用次数: 2

摘要

在过去三十年的文献中充满了视网膜图像分析的自动方法。由于后处理或调优需求可能与原始手动方法一样耗时,因此接受度受到限制。本文的观点是,通过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可以在实践和理论上实现。因此,对感兴趣的区域进行简单的标记,人类可以在几分钟内接受并轻松执行,这可以为已经开发良好的算法提供巨大的优势。提供了三个例子:结节分割、图像配准和地理萎缩分割,以及在疾病理解中的应用。
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Interactive Image Analysis in Age-related Macular Degeneration (AMD) and Stargardt Disease (STGD).

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.

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