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Automated lesion analysis based upon automatic plaque characterization according to a classification criterion

机译:根据分类标准基于斑块自动表征的自动病变分析

摘要

A system and method are disclosed for automatically classifying plaque lesions. A plaque classification application applies a plaque classification criterion to at least one graphical image, comprising a map of spectrally-analyzed characterized tissue of a vessel cross-section, to render an overall plaque classification for the slice or set of slices, covering a 3D volume. The plaque classification is based upon the amount and location of each characterized tissue type (e.g., necrotic core—NC). In an exemplary embodiment the set of potential plaque classifications, not to be confused with characterized tissue types—from which the plaque classifications are derived—include, for example: adaptive intimal thickening (AIT), pathological intimal thickening (PIT), fibroatheroma (FA), thin-cap fibroatheroma (TCFA), and fibro-calcific (FC).
机译:公开了一种用于自动分类斑块病变的系统和方法。斑块分类应用程序将斑块分类标准应用于至少一个图形图像,该图形图像包括脉管横截面的经光谱分析的特征组织图,以对切片或切片组进行整体斑块分类,覆盖3D体积。斑块分类是基于每种特征化的组织类型(例如,坏死核-NC)的数量和位置。在示例性实施例中,请勿将潜在斑块分类的集合与特征性组织类型相混淆,从该组织类型中衍生出斑块分类,包括例如:适应性内膜增厚(AIT),病理性内膜增厚(PIT),纤维性动脉瘤(FA) ),薄帽纤维化动脉瘤(TCFA)和纤维钙化(FC)。

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