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迈向非增强CT图像中心外膜脂肪的自动定量分析

Towards automatic quantification of the epicardial fat in non-contrasted CT images.

作者信息

Barbosa Jorge G, Figueiredo Bruno, Bettencourt Nuno, Tavares João Manuel R S

机构信息

Laboratório de Inteligência Artificial e Ciência dos Computadores, Departamento de Engenharia Informática, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.

出版信息

Comput Methods Biomech Biomed Engin. 2011 Oct;14(10):905-14. doi: 10.1080/10255842.2010.499871. Epub 2011 Jun 1.

Abstract

In this work, we present a technique to semi-automatically quantify the epicardial fat in non-contrasted computed tomography (CT) images. The epicardial fat is very close to the pericardial fat, being separated only by the pericardium that appears in the image as a very thin line, which is hard to detect. Therefore, an algorithm that uses the anatomy of the heart was developed to detect the pericardium line via control points of the line. From the points detected an interpolation was applied based on the cubic interpolation, which was also improved to avoid incorrect interpolation that occurs when the two variables are non-monotonic. The method is validated by using a set of 40 CT images of the heart of 40 human subjects. In 62.5% of the cases only minimal user intervention was required and the results compared favourably with the results obtained by the manual process.

摘要

在这项工作中,我们提出了一种在非增强计算机断层扫描(CT)图像中半自动量化心外膜脂肪的技术。心外膜脂肪非常接近心包脂肪,仅由在图像中表现为极细线条的心包分隔开,而这条线很难检测到。因此,开发了一种利用心脏解剖结构的算法,通过该线条的控制点来检测心包线。从检测到的点出发,基于三次样条插值进行应用,并且对其进行了改进,以避免在两个变量非单调时出现的错误插值。该方法通过使用一组40名人类受试者心脏的40张CT图像进行了验证。在62.5%的病例中,仅需要极少的用户干预,并且结果与手动处理获得的结果相比具有优势。

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