Zhang Daoqiang, Wu Guorong, Jia Hongjun, Shen Dinggang
Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):643-50. doi: 10.1007/978-3-642-23626-6_79.
Label fusion is a key step in multi-atlas based segmentation, which combines labels from multiple atlases to make the final decision. However, most of the current label fusion methods consider each voxel equally and independently during label fusion. In our point of view, however, different voxels act different roles in the way that some voxels might have much higher confidence in label determination than others, i.e., because of their better alignment across all registered atlases. In light of this, we propose a sequential label fusion framework for multi-atlas based image segmentation by hierarchically using the voxels with high confidence to guide the labeling procedure of other challenging voxels (whose registration results among deformed atlases are not good enough) to afford more accurate label fusion. Specifically, we first measure the corresponding labeling confidence for each voxel based on the k-nearest-neighbor rule, and then perform label fusion sequentially according to the estimated labeling confidence on each voxel. In particular, for each label fusion process, we use not only the propagated labels from atlases, but also the estimated labels from the neighboring voxels with higher labeling confidence. We demonstrate the advantage of our method by deploying it to the two popular label fusion algorithms, i.e., majority voting and local weighted voting. Experimental results show that our sequential label fusion method can consistently improve the performance of both algorithms in terms of segmentation/labeling accuracy.
标签融合是基于多图谱分割中的关键步骤，它将来自多个图谱的标签进行合并以做出最终决策。然而，当前大多数标签融合方法在标签融合过程中对每个体素都同等且独立地进行考虑。然而，在我们看来，不同体素起着不同的作用，因为某些体素在标签确定方面可能比其他体素具有更高的置信度，即由于它们在所有配准图谱上的对齐效果更好。鉴于此，我们提出了一种基于多图谱的图像分割的顺序标签融合框架，通过分层使用高置信度的体素来指导其他具有挑战性的体素（其在变形图谱间的配准结果不够好）的标记过程，以实现更准确的标签融合。具体而言，我们首先基于k近邻规则测量每个体素的相应标记置信度，然后根据对每个体素估计的标记置信度依次进行标签融合。特别地，对于每个标签融合过程，我们不仅使用来自图谱的传播标签，还使用来自具有更高标记置信度的相邻体素的估计标签。我们通过将我们的方法应用于两种流行的标签融合算法，即多数投票和局部加权投票，来证明我们方法的优势。实验结果表明，我们的顺序标签融合方法在分割/标记准确性方面能够持续提高这两种算法的性能。
