医学影像学中的定量方法

M. Loew
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摘要

由于以下几个原因,医学图像的测量和比较变得越来越重要:(1)随着医学成像变得越来越数字化,图像网络和档案的激增,机会将创造需求;(2)治疗设计、剂量测量和手术计划的自动化方法正在从实验室出现,并进入有限的临床应用;(3)越来越多的用户希望采用自动化方法提供的可靠和可重复的方法。图像处理工具给我们一些形状、大小、纹理、颜色、深度和三维特征。结合成像方式的特性和解剖学知识,它们产生了对鉴别诊断有用的定量描述。然而,很少受到注意的是需要对现有的各种方法进行基准和评价。几乎没有采取任何措施来确保报告结果的可比性。用户界面,这可能是临床医生与应用系统的唯一接触,并没有要求系统设计者比基准测试更多的研究。随着需要证明成像费用的增长,必须对其效益进行分析,但在评估成像系统的良好标准存在之前,不可能有可靠的方法来衡量结果。同样,如果没有这样的标准,使用其他网站的图像进行教学和研究也会受到阻碍。本文概述了这个问题,并提出了一些可以采取的措施,使医学成像真正量化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantitative methods in medical imaging
Measurement and comparison of medical images is of growing importance for several reasons: (1) as medical imaging becomes ever more digital, and networks and archives of images proliferate, the opportunity will create the need; (2) automated approaches to treatment design, dose measurement, and surgery planning are emerging from the laboratory and entering limited clinical use; and (3) a greater variety of users wants to employ the reliable and repeatable methodology that seems to be offered by the automated methods. Image-processing tools give us some characterization of shape, size, texture, color, depth and three-dimensionality. Combined with properties of the imaging modality and knowledge of anatomy, they yield quantitative descriptions that are useful in differential diagnosis. What has received little attention, however, is the need for benchmarking and evaluation of the various methods available. Almost nothing has been done to ensure the comparability of reported results. The user interface, which may be the clinician's only contact with the application system, has not claimed appreciably more study by system designers than benchmarking. As the need grows to justify the expense of imaging, analysis of its benefits will have to be measured, but until good criteria exist for assessing the imaging system, there cannot be a reliable way to measure outcomes. Equally, use of images from other sites for teaching and research will be impeded in the absence of such metrics. This paper outlines the problem and suggests some steps that can be taken to bring real quantification to medical imaging.<>
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