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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Level Set Segmentation of the Heart from 4D Phase Contrast MRI
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Level Set Segmentation of the Heart from 4D Phase Contrast MRI

机译：通过4D相衬MRI对心脏进行水平集分割

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摘要

Blood flow properties in the heart can be examined non invasively by means of Phase Contrast MRI (PC MRI), an imaging technique that provides not only morphology images but also velocity information. We present a novel feature combination for level set segmentation of the heart's cavities in multidirectional 4D PC MRI data. The challenge in performing the segmentation task successfully in this context is first of all the bad image quality, as compared to classical MRI. As generally in heart segmentation, the intra and inter subject variability of the heart has to be coped with as well. The central idea of our approach is to integrate a set of essentially differing sources of information into the segmentation process to make it capable of handling qualitatively bad and highly varying data. To the best of our knowledge our system is the first to concurrently incorporate a flow measure as well as a priori shape knowledge into a level set framework in addition to the commonly used edge and curvature information. The flow measure is derived from PC MRI velocity data. As shape knowledge we use a 3D shape of the respective cavity. We validated our system design by a series of qualitative performance tests. The combined use of shape knowledge and a flow measure increases segmentation quality compared to results obtained by using only one of those features. A first clinical study was performed on two 4D datasets, from which we segmented the left ventricle and atrium. The segmentation results were examined by an expert and judged suitable for use in clinical practice.
机译：心脏的血流特性可以通过相衬MRI（PC MRI）进行无创检查，该成像技术不仅提供形态图像，而且还提供速度信息。我们提出了一种新颖的功能组合，用于多方向4D PC MRI数据中心脏腔的水平集分割。与传统的MRI相比，在这种情况下成功执行分割任务所面临的挑战首先是图像质量差。如通常在心脏分割中一样，还必须应对心脏的受试者内部和受试者之间的变异性。我们方法的中心思想是将一组本质上不同的信息源集成到细分过程中，以使其能够处理质量上差且变化很大的数据。据我们所知，我们的系统是第一个将流量度量以及先验形状知识同时并入到水平集框架中的系统，除了常用的边缘和曲率信息。流量测量值是从PC MRI速度数据得出的。作为形状知识，我们使用相应空腔的3D形状。我们通过一系列定性性能测试验证了我们的系统设计。与仅使用那些特征之一获得的结果相比，形状知识和流量度量的组合使用可提高分割质量。在两个4D数据集上进行了首次临床研究，从中我们将左心室和心房分割开。分割结果由专家检查，并判断适合用于临床实践。

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