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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Automatic 4D-Segmentation of the Left Ventricle in Cardiac-CT-Data
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Automatic 4D-Segmentation of the Left Ventricle in Cardiac-CT-Data

机译：心脏CT数据中左心室的自动4D分割

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摘要

The manual segmentation and analysis of 4D high resolution multi slice cardiac CT datasets is both labor intensive and time consuming. Therefore, it is necessary to supply the cardiologist with powerful software tools, to segment the myocardium and the cardiac cavities in all cardiac phases and to compute the relevant diagnostic parameters. In recent years there have been several publications concerning the segmentation and analysis of the left ventricle (LV) and myocardium for a single phase or for the diagnostically most relevant phases, the enddiastole (ED) and the endsystole (ES). However, for a complete diagnosis and especially of wall motion abnormalities, it is necessary to analyze not only the motion endpoints ED and ES, but also all phases in-between.rnIn this paper a novel approach for the 4D segmentation of the left ventricle in cardiac-CT-data is presented. The segmentation of the 4D data is divided into a first part, which segments the motion endpoints of the cardiac cycle ED and ES and a second part, which segments all phases in-between. The first part is based on a bi-temporal statistical shape model of the left ventricle. The second part uses a novel approach based on the individual volume curve for the interpolation between ED and ES and afterwards an active contour algorithm for the final segmentation. The volume curve based interpolation step allows the constraint of the subsequent segmentation of the phases between ED and ES to very small search-intervals, hence makes the segmentation process faster and more robust.
机译：手动分割和分析4D高分辨率多层心脏CT数据集既费力又费时。因此，有必要为心脏病专家提供功能强大的软件工具，以在所有心脏阶段对心肌和心脏腔进行分段，并计算相关的诊断参数。近年来，已经有一些出版物涉及左心室（LV）和心肌的单相或诊断上最相关的阶段（舒张期（ED）和收缩期（ES））的分割和分析。然而，为了进行完整的诊断，尤其是壁运动异常的诊断，不仅需要分析运动终点ED和ES，还需要分析运动端点之间的所有相位。本文采用一种新颖的方法对左心室进行4D分割显示了心脏CT数据。 4D数据的分割分为第一部分和第二部分，第一部分对心动周期ED和ES的运动端点进行分割，第二部分对两者之间的所有相进行分割。第一部分基于左心室的双时态统计形状模型。第二部分使用基于个体体积曲线的新颖方法在ED和ES之间进行插值，然后使用主动轮廓算法进行最终分割。基于体积曲线的插值步骤允许将ED和ES之间的相继分段限制为非常小的搜索间隔，从而使分段过程更快，更可靠。

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