Yang Yan, Tannenbaum Allen, Giddens Don, Coulter Wallace
Wallace H. Coulter Department of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, GA 30332, USA.
Conf Proc IEEE Eng Med Biol Soc. 2004;2004:1664-6. doi: 10.1109/IEMBS.2004.1403502.
An approach for the 3D segmentation and reconstruction of human left coronary arteries using angio-CT images is presented in This work. Each voxel in the 3D dataset is assumed to belong to one of the three homogeneous regions: blood, myocardium, and lung. A priori knowledge of the regions is introduced via Bayes' rule. Posterior probabilities obtained using Bayes' rule are anisotropically smoothed, and the 3D segmentation is obtained via MAP classifications of the smoothed posteriors. An active contour model is then applied to extract the coronary arteries from the rest of the volumetric data with subvoxel accuracy. The geometric model of the left coronary arteries obtained in this work may be used to provide accurate boundary conditions for hemodynamic simulations, or to provide objective measurements of clinically relevant parameters such as lumen sizes in a 3D sense.
本文提出了一种利用血管CT图像对人类左冠状动脉进行三维分割和重建的方法。三维数据集中的每个体素被假定属于三个同质区域之一：血液、心肌和肺。通过贝叶斯法则引入区域的先验知识。使用贝叶斯法则获得的后验概率进行各向异性平滑处理，并通过平滑后验的最大后验概率（MAP）分类获得三维分割。然后应用主动轮廓模型以亚体素精度从体积数据的其余部分中提取冠状动脉。在这项工作中获得的左冠状动脉几何模型可用于为血流动力学模拟提供准确的边界条件，或用于在三维意义上提供诸如管腔大小等临床相关参数的客观测量。
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