低剂量心脏CT图像中心肌的点引导建模与分割
Point-guided modeling and segmentation of myocardium for low dose cardiac CT images.
作者信息
Liu Yixun, Nacif Marcelo Souto, Liu Songtao, Sibley Christopher T, Bluemke David A, Summers Ronald M, Yao Jianhua
机构信息
Radiology and Imaging Science, National Institutes of Health, MD, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5327-30. doi: 10.1109/EMBC.2012.6347197.
Cardiac CT is emerging as a preferable modality to detect myocardial stress/rest perfusion; however the insufficient contrast of myocardium on CT image makes its segmentation difficult. In this paper, we present a point-guided modeling and deformable model-based segmentation method. This method first builds a triangular surface model of myocardium through Bézier contour fitting based on a few points selected by clinicians. Then, a deformable model-based segmentation method is developed to refine the segmentation result. The experiments on 8 cases show the accuracy of the segmentation in terms of true positive volume fraction, false positive volume fractions, and average surface distance can reach 91.0%, 0.3%, and 0.6mm, respectively. The comparison between the proposed method and a graph cut-based method is performed. The results demonstrate that this method is effective in improving the accuracy further.
心脏CT正逐渐成为检测心肌负荷/静息灌注的一种优选方式；然而，CT图像中心肌对比度不足使其分割变得困难。在本文中，我们提出了一种基于点引导建模和可变形模型的分割方法。该方法首先基于临床医生选择的几个点，通过贝塞尔轮廓拟合构建心肌的三角表面模型。然后，开发了一种基于可变形模型的分割方法来细化分割结果。对8个病例的实验表明，在真阳性体积分数、假阳性体积分数和平均表面距离方面，分割的准确率分别可达91.0%、0.3%和0.6毫米。对所提方法与基于图割的方法进行了比较。结果表明，该方法在进一步提高准确率方面是有效的。
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