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首页> 外文期刊>NeuroImage: Clinical >Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting
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Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting

机译：基于多图谱投票的解剖MRI直接估计患者属性

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MRI brain atlases are widely used for automated image segmentation, and in particular, recent developments in multi-atlas techniques have shown highly accurate segmentation results. In this study, we extended the role of the atlas library from mere anatomical reference to a comprehensive knowledge database with various patient attributes, such as demographic, functional, and diagnostic information. In addition to using the selected (heavily-weighted) atlases to achieve high segmentation accuracy, we tested whether the non-anatomical attributes of the selected atlases could be used to estimate patient attributes. This can be considered a context-based image retrieval (CBIR) approach, embedded in the multi-atlas framework. We first developed an image similarity measurement to weigh the atlases on a structure-by-structure basis, and then, the attributes of the multiple atlases were weighted to estimate the patient attributes. We tested this concept first by estimating age in a normal population; we then performed functional and diagnostic estimations in Alzheimer's disease patients. The accuracy of the estimated patient attributes was measured against the actual clinical data, and the performance was compared to conventional volumetric analysis. The proposed CBIR framework by multi-atlas voting would be the first step toward a knowledge-based support system for quantitative radiological image reading and diagnosis. Highlights ? Patient attributes are estimated directly by retrieving information from multi-atlas. ? Non-imaging attributes of the atlases are weighted to estimate patient attributes. ? The method achieved high accuracy in estimating age in the normal population. ? The method can estimate functional and diagnostic attributes in dementia patients. ? The estimation accuracy was higher than volumetric analysis in subcortical areas.
机译：MRI脑图谱已广泛用于自动图像分割，尤其是多图谱技术的最新发展已显示出高度准确的分割结果。在这项研究中，我们将地图集库的作用从单纯的解剖学参考扩展到了具有各种患者属性（例如人口统计，功能和诊断信息）的综合知识数据库。除了使用选定的（权重较大的）地图集来实现高分割精度外，我们还测试了选定地图集的非解剖属性是否可以用于估计患者属性。可以将其视为嵌入多图集框架中的基于上下文的图像检索（CBIR）方法。我们首先开发了一种图像相似性测量方法，以逐个结构加权地图集，然后，对多个地图集的属性进行加权，以估计患者的属性。我们首先通过估计正常人群的年龄来测试该概念。然后，我们对阿尔茨海默氏病患者进行了功能和诊断评估。对照实际临床数据测量了估计患者属性的准确性，并将其性能与常规容量分析进行了比较。通过多图册投票提出的CBIR框架将是迈向基于知识的定量放射图像读取和诊断支持系统的第一步。强调 ？通过从多图谱中检索信息可以直接估计患者的属性。 ？地图集的非成像属性被加权以估计患者属性。 ？该方法在估计正常人群的年龄方面达到了高精度。 ？该方法可以估计痴呆患者的功能和诊断属性。 ？皮层下区域的估计准确性高于体积分析。

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