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虚拟 M 型超声心动图:一种二尖瓣前叶分段的新方法。

Virtual M-Mode for Echocardiography: A New Approach for the Segmentation of the Anterior Mitral Leaflet.

出版信息

IEEE J Biomed Health Inform. 2019 Jan;23(1):305-313. doi: 10.1109/JBHI.2018.2799738. Epub 2018 Jan 30.

DOI:10.1109/JBHI.2018.2799738
PMID:29994568
Abstract

Rheumatic heart disease can result from repeated episodes of acute rheumatic fever, which damages the heart valves and reduces their functionality. Early manifestations of heart valve damage are visible in echocardiography in the form of valve thickening, shape changing and mobility reduction. The quantification of these features is important for a precise diagnosis and it is the main motivation for this work. The first step to make this quantification is to accurately identify and track the anterior mitral leaflet throughout the cardiac cycle. An accurate segmentation and tracking with minimum user interaction is still an open problem in literature due to low image quality, speckle noise, signal dropout and nonrigid deformations. In this work, we propose a novel approach for the identification of the anterior mitral valve leaflet in all frames. The method requires a single user-specified point on the posterior wall of the aorta as input, in the first frame. The echocardiography videos are converted into a new image space, the Virtual M-mode, which samples the original echocardiography image over automatically estimated scanning lines. This new image space not only provides the motion pattern of the posterior wall of the aorta, the anterior wall of the aorta and the posterior wall of the left atrium, but also provides the location of the structures in each frame. The location information is then used to initialize the localized active contours, followed by segmenting the anterior mitral leaflet. Results shown that the new image space has robustly identified the anterior mitral valve leaflet, without any failure. The median modified Hausdorff distance error of the proposed method was 2.3 mm, with a recall of 0.94.

摘要

风湿性心脏病可由反复发生的急性风湿热引起,这种疾病会损害心脏瓣膜并降低其功能。心脏瓣膜损伤的早期表现可在超声心动图中以瓣膜增厚、形态改变和活动度降低的形式显现。这些特征的定量对于准确诊断非常重要,这也是这项工作的主要动机。实现这种定量的第一步是在整个心动周期内准确识别和跟踪二尖瓣前叶。由于图像质量低、斑点噪声、信号丢失和非刚性变形等原因,在文献中,实现准确的分割和跟踪而无需用户进行最小交互仍然是一个开放问题。在这项工作中,我们提出了一种新颖的方法来识别所有帧中的二尖瓣前叶。该方法仅需要用户在第一帧中指定主动脉后壁上的单个点作为输入。超声心动图视频被转换为一个新的图像空间,即虚拟 M 模式,该模式在自动估计的扫描线上对原始超声心动图图像进行采样。这个新的图像空间不仅提供了主动脉后壁、主动脉前壁和左心房后壁的运动模式,还提供了每个帧中结构的位置信息。然后,利用该位置信息初始化局部化活动轮廓,进而分割二尖瓣前叶。结果表明,新的图像空间可以稳健地识别二尖瓣前叶,没有任何失败。所提出方法的平均改进 Hausdorff 距离误差为 2.3 毫米,召回率为 0.94。

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