Adaptive local multi-atlas segmentation: Application to the heart and the caudate nucleus
Adaptive local multi-atlas segmentation: Application to the heart and the caudate nucleus
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DOI：
10.1016/j.media.2009.10.001
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发表时间：
2010-02-01
影响因子：
10.9
通讯作者：
van Ginneken, Bram
中科院分区：
文献类型：
--
作者：
van Rikxoort, Eva M.;Isgum, Ivana;van Ginneken, Bram
基于血管的分割是一种强大的通用技术，用于自动描绘体积图像中的结构。一些研究表明，多图谱分割方法优于仅使用单个图谱的方案，但在体积数据上运行多个配准是耗时的。此外，对于许多扫描或扫描内的区域，可能不需要大量的图谱来实现良好的分割性能，甚至可能使结果恶化。因此，在分割过程中包括决定使用哪些和多少图集用于特定目标扫描是值得的。为此，我们提出了两种普遍适用的多图谱分割方法，自适应多图谱分割（AMAS）和自适应局部多图谱分割（ALMAS）。AMAS会自动为目标图像选择最合适的地图集，并在预期没有进一步改进时自动停止配准地图集。ALMAS通过局部决定分割目标图像需要多少个地图集和哪些地图集来进一步实现这一概念。该方法采用了一个计算便宜的图集选择策略，自动停止标准，和一种技术，以本地检查配准结果，并确定有多大的改善，可以预期从进一步registration.AMAS和ALMAS应用到分割的心脏在胸部的计算机断层扫描，并比较到传统的多图集方法（MAS）。实验结果表明，ALMAS算法以更低的计算代价获得了与MAS算法相同的性能。当可用分割时间固定时，AMAS和ALMAS的性能明显优于MAS。此外，AMAS被应用于脑MRI扫描中描绘尾状核的在线分割挑战，在迄今为止提交的所有结果中获得了最佳评分。(C)2009年爱思唯尔B。V.保留所有权利。
Atlas-based segmentation is a powerful generic technique for automatic delineation of structures in volumetric images. Several studies have shown that multi-atlas segmentation methods outperform schemes that use only a single atlas, but running multiple registrations on volumetric data is time-consuming. Moreover, for many scans or regions within scans, a large number of atlases may not be required to achieve good segmentation performance and may even deteriorate the results. It would therefore be worthwhile to include the decision which and how many atlases to use for a particular target scan in the segmentation process. To this end, we propose two generally applicable multi-atlas segmentation methods, adaptive multi-atlas segmentation (AMAS) and adaptive local multi-atlas segmentation (ALMAS). AMAS automatically selects the most appropriate atlases for a target image and automatically stops registering atlases when no further improvement is expected. ALMAS takes this concept one step further by locally deciding how many and which atlases are needed to segment a target image. The methods employ a computationally cheap atlas selection strategy, an automatic stopping criterion, and a technique to locally inspect registration results and determine how much improvement can be expected from further registrations.AMAS and ALMAS were applied to segmentation of the heart in computed tomography scans of the chest and compared to a conventional multi-atlas method (MAS). The results show that ALMAS achieves the same performance as MAS at a much lower computational cost. When the available segmentation time is fixed, both AMAS and ALMAS perform significantly better than MAS. In addition, AMAS was applied to an online segmentation challenge for delineation of the caudate nucleus in brain MRI scans where it achieved the best score of all results submitted to date. (C) 2009 Elsevier B. V. All rights reserved.