A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography.
A Dynamical Shape Prior for LV Segmentation from RT3D Echocardiography.
复制标题
RT3D 超声心动图左室分割的动态形状先验。
DOI：
10.1007/978-3-642-04268-3_26
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Duncan,JamesS
中科院分区：
文献类型：
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作者：
Zhu,Yun;Papademetris,Xenophon;Sinusas,AlbertJ;Duncan,JamesS
实时三维超声心动图（RT3D）是最新一代的三维超声心动图。RT 3D超声心动图图像的分割对于确定许多重要的诊断参数至关重要。在心脏成像中，由于心脏是一个运动的器官，关于其形状和运动模式的先验知识成为分割任务的重要组成部分。然而，大多数先前的心脏模型是静态模型（SM），其忽略心脏序列的时间相干性或通用动态模型（GDM），其忽略心脏运动的受试者间变异性。在本文中，我们提出了一个主题特定的动力学模型（SSDM），同时处理受试者间的变异性和心脏动力学（受试者内变异性）。它可以根据过去帧中观察到的形状逐步预测当前帧处新序列的形状和运动模式。将此SSDM的分割过程中制定的递归贝叶斯框架。这导致基于当前帧的强度信息以及基于来自先前帧的预测的每个帧的分割。15个RT3D超声心动图序列的定量结果表明，SSDM的自动分割上级SM或GDM，与手动分割相当。
Real-time three-dimensional (RT3D) echocardiography is the newest generation of three-dimensional (3-D) echocardiography. Segmentation of RT3D echocardiographic images is essential for determining many important diagnostic parameters. In cardiac imaging, since the heart is a moving organ, prior knowledge regarding its shape and motion patterns becomes an important component for the segmentation task. However, most previous cardiac models are either static models (SM), which neglect the temporal coherence of a cardiac sequence or generic dynamical models (GDM), which neglect the inter-subject variability of cardiac motion. In this paper, we present a subject-specific dynamical model (SSDM) which simultaneously handles inter-subject variability and cardiac dynamics (intra-subject variability). It can progressively predict the shape and motion patterns of a new sequence at the current frame based on the shapes observed in the past frames. The incorporation of this SSDM into the segmentation process is formulated in a recursive Bayesian framework. This results in a segmentation of each frame based on the intensity information of the current frame, as well as on the prediction from the previous frames. Quantitative results on 15 RT3D echocardiographic sequences show that automatic segmentation with SSDM is superior to that of either SM or GDM, and is comparable to manual segmentation.