Lynch Michael, Ghita Ovidiu, Whelan Paul F
Siemens AG, 91058 Erlangen, Germany.
IEEE Trans Med Imaging. 2008 Feb;27(2):195-203. doi: 10.1109/TMI.2007.904681.
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense.
现代医学成像模态在空间和时间域中都提供了大量信息，而将这些信息整合到一个连贯的算法框架中是一项重大挑战。在本文中，我们提出了一种新颖且直观的方法，用于在集成分割算法中结合三维空间和时间（三维 + 时间）磁共振成像（MRI）数据，以提取左心室的心肌。我们开发了一种新颖的水平集分割过程，可同时描绘和跟踪左心室肌肉的边界。通过在参数框架中编码有关心脏时间演变的先验知识，期望最大化算法可在心动周期内最佳地跟踪心肌变形。期望步骤使水平集函数变形，而最大化步骤更新先前的时间模型参数，以在非刚性意义上执行分割。
