Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data
Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data
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DOI：
10.1109/tmi.2009.2014372
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发表时间：
2009-08-01
影响因子：
10.6
通讯作者：
Ortiz-de-Solorzano, Carlos
中科院分区：
文献类型：
--
作者：
Artaechevarria, Xabier;Munoz-Barrutia, Arrate;Ortiz-de-Solorzano, Carlos
研究表明，在基于地图集的医学图像分割中，采用多幅地图集图像可以提高分割精度。将每张地图集图像独立配准到目标图像上，并将计算得到的变换应用于地图集图像的分割，得到目标图像的分割版本。这个过程产生了几个独立的候选分割，它们必须以某种方式组合成一个最终的分割。多数表决是融合分割的常用规则，但也有人提出了更复杂的方法。在本文中，我们表明，使用全局权重来考虑候选分割有一个主要的局限性。为了提高分割精度，我们提出了广义局部加权投票法。即，融合权值根据分割性能的局部估计逐体素进行调整。利用人类大脑的数字幻影和MR图像，我们证明了每种组合技术的性能取决于分割区域的灰度对比度特征，并且没有一种融合方法在所有区域都能产生比其他融合方法更好的结果。特别是，我们表明局部组合策略在分割高对比度结构时优于全局方法，而当相邻结构之间的对比度较低时，全局方法对噪声不太敏感。我们得出结论，为了达到最高的整体分割精度，必须选择每个特定结构的最佳组合方法。
It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.