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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Adaptive Local Multi-Atlas Segmentation: Application to Heart Segmentation in Chest CT Scans
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Adaptive Local Multi-Atlas Segmentation: Application to Heart Segmentation in Chest CT Scans

机译：自适应局部多图谱分割：在胸部CT扫描中应用于心脏分割

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摘要

Atlas-based segmentation is a popular generic technique for automated delineation of structures in volumetric data sets. Several studies have shown that multi-atlas based segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on large volumetric data is too time-consuming for routine clinical use. We propose a generally applicable adaptive local multi-atlas segmentation method (ALMAS) that locally decides how many and which atlases are needed to segment a target image. Only the selected parts of atlases are registered. The method is iterative and automatically stops when no further improvement is expected. ALMAS was applied to segmentation of the heart on chest CT scans and compared to three existing atlas-based methods. It performed significantly better than single-atlas methods and as good as multi-atlas methods at a much lower computational cost.
机译：基于图集的分割是一种流行的通用技术，用于自动描绘体积数据集中的结构。多项研究表明，基于多图集的分割方法优于仅使用单个图集的方案，但是对于常规临床使用而言，在大容量数据上运行多个配准太耗时。我们提出了一种普遍适用的自适应局部多图集分割方法（ALMAS），该方法可以本地决定分割目标图像所需的图集数量和哪些图集。仅注册了地图集的选定部分。该方法是迭代的，并且在不需要进一步改进时会自动停止。 ALMAS被应用于胸部CT扫描的心脏分割，并与现有的三种基于图谱的方法进行了比较。与单图集方法相比，它的执行效果明显好于多图集方法，而计算成本却低得多。

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