Multi-Atlas-Based Segmentation With Local Decision Fusion-Application to Cardiac and Aortic Segmentation in CT Scans
Multi-Atlas-Based Segmentation With Local Decision Fusion-Application to Cardiac and Aortic Segmentation in CT Scans
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DOI：
10.1109/tmi.2008.2011480
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发表时间：
2009-07-01
影响因子：
10.6
通讯作者：
van Ginneken, Brain
中科院分区：
文献类型：
--
作者：
Isgum, Ivana;Staring, Marius;van Ginneken, Brain
提出了一种基于多配准组合的基于地图集的图像分割方法。多个地图集注册到目标图像。为了获得目标的分割，将地图集图像的标签传播给目标。传播的标签通过空间变化的决策融合权重进行组合。这些权重来源于对注册成功的本地评估。在此基础上，提出了一种相当于统计模式识别理论中序列前向选择的图谱选择方法。将该方法与现有的三种基于图谱的分割方法(1)基于单一图谱的分割方法、2)基于平均形状图谱的分割方法和3)基于平均作为决策融合的多图谱分割方法进行了比较。这些方法在胸部的计算机断层扫描中对心脏和主动脉的分割进行了测试。结果表明，所提出的方法优于其他方法，并且产生的结果非常接近独立的人类观察者的结果。此外，额外的图谱选择步骤可以在相当的性能下实现更快的分割。
A novel atlas-based segmentation approach based on the combination of multiple registrations is presented. Multiple atlases are registered to a target image. To obtain a segmentation of the target, labels of the atlas images are propagated to it. The propagated labels are combined by spatially varying decision fusion weights. These weights are derived from local assessment of the registration success. Furthermore, an atlas selection procedure is proposed that is equivalent to sequential forward selection from statistical pattern recognition theory. The proposed method is compared to three existing atlas-based segmentation approaches, namely 1) single atlas-based segmentation, 2) average-shape atlas-based segmentation, and 3) multi-atlas-based segmentation with averaging as decision fusion. These methods were tested on the segmentation of the heart and the aorta in computed tomography scans of the thorax. The results show that the proposed method outperforms other methods and yields results very close to those of an independent human observer. Moreover, the additional atlas selection step led to a faster segmentation at a comparable performance.