Yushkevich Paul A, Wang Hongzhi, Pluta John, Avants Brian B
Department of Radiology, University of Pennsylvania, Philadelphia, USA.
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2012:956-963. doi: 10.1109/CVPR.2012.6247771.
Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by registering the image to a set of atlases [19, 11, 8]. Effective label fusion strategies, such as local similarity-weighted voting [1, 13] substantially reduce segmentation errors compared to single-atlas segmentation. This paper extends the label fusion idea to the problem of finding correspondences across a set of images. Instead of computing a consensus segmentation, weighted voting is used to estimate a consensus coordinate map between a target image and a reference space. Two variants of the problem are considered: (1) where correspondences between a set of atlases are known and are propagated to the target image; (2) where correspondences are estimated across a set of images without prior knowledge. Evaluation in synthetic data shows that correspondences recovered by fusion methods are more accurate than those based on registration to a population template. In a 2D example in real MRI data, fusion methods result in more consistent mappings between manual segmentations of the hippocampus.
标签融合策略用于多图谱图像分割方法中，在给定通过将图像配准到一组图谱而产生的一组候选分割结果的情况下，计算图像的一致分割结果[19, 11, 8]。与单图谱分割相比，有效的标签融合策略，如局部相似性加权投票[1, 13]，能大幅减少分割误差。本文将标签融合思想扩展到在一组图像中寻找对应关系的问题。不是计算一致分割结果，而是使用加权投票来估计目标图像和参考空间之间的一致坐标图。考虑了该问题的两种变体：(1) 一组图谱之间的对应关系已知并传播到目标图像的情况；(2) 在没有先验知识的情况下，跨一组图像估计对应关系的情况。在合成数据中的评估表明，通过融合方法恢复的对应关系比基于配准到总体模板的对应关系更准确。在真实MRI数据的一个二维示例中，融合方法在海马体的手动分割之间产生了更一致的映射。
