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Morphological Analysis of the Left Ventricular Endocardial Surface and Its Clinical Implications

机译:左心室内膜表面的形态学分析及其临床意义

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The complex morphological structure of the left ventricular endocardial surface and its relation to the severity of arterial stenosis has not yet been thoroughly investigated due to the limitations of conventional imaging techniques. By exploiting the recent developments in Multirow-Detector Computed Tomography (MDCT) scanner technology, the complex endocardial surface morphology of the left ventricle is studied and the cardiac segments affected by coronary arterial stenosis localized via analysis of Computed Tomography (CT) image data obtained from a 320-MDCT scanner. The non-rigid endocardial surface data is analyzed using an isometry-invariant Bag-of-Words (BOW) feature-based approach. The clinical significance of the analysis in identifying, localizing and quantifying the incidence and extent of coronary artery disease is investigated. Specifically, the association between the incidence and extent of coronary artery disease and the alterations in the endocardial surface morphology is studied. The results of the proposed approach on 15 normal data sets, and 12 abnormal data sets exhibiting coronary artery disease with varying levels of severity are presented. Based on the characterization of the endocardial surface morphology using the Bag-of-Words features, a neural network-based classifier is implemented to test the effectiveness of the proposed morphological analysis approach. Experiments performed on a strict leave-one-out basis are shown to exhibit a distinct pattern in terms of classification accuracy within the cardiac segments where the incidence of coronary arterial stenosis is localized.
机译:左心室心内膜表面及其与动脉狭窄的严重程度的复杂的形态结构尚未彻底由于传统的成像技术的限制的影响。通过利用在多排探测器计算机断层摄影(MDCT)扫描技术的最近发展中,左心室的复杂心内膜表面形态从获得的图像数据进行了研究并受其影响的冠状动脉狭窄的心脏节段通过计算机断层扫描(CT)的分析局部320-MDCT扫描仪。非刚性心内膜表面数据是使用等距不变袋的字(BOW)基于特征的方法进行分析。分析在确定,定位和定量冠状动脉疾病的发生率和程度的临床意义进行了研究。具体而言,发生率和冠状动脉疾病的程度和心内膜表面形态的变动之间的关联进行了研究。 15个正常的数据集,并且显示出冠状动脉疾病严重性的不同水平的12个异常数据集所提出的方法的结果。基于使用袋的字的特征的心内膜表面形态的表征,一个基于神经网络的分类器实施以测试提出的形态分析方法的有效性。实验上的严格的留一法的基础上执行显示在其中冠状动脉狭窄的发生率被定位的心脏节段内的分类精确度方面表现出不同的图案。

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