From label fusion to correspondence fusion: a new approach to unbiased groupwise registration.
From label fusion to correspondence fusion: a new approach to unbiased groupwise registration.
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DOI：
10.1109/cvpr.2012.6247771
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
Avants BB
中科院分区：
文献类型：
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作者：
Yushkevich PA;Wang H;Pluta J;Avants BB
标签融合策略用于多图集图像分割方法中，以计算图像的一致分割，给定通过将图像配准到一组图集而产生的一组候选分割。与单图集分割相比，有效的标签融合策略（例如局部相似性加权投票）大大减少了分割错误。本文将标签融合思想扩展到寻找一组图像之间的对应关系的问题。使用加权投票来估计目标图像和参考空间之间的共识坐标图，而不是计算共识分割。考虑该问题的两种变体：（1）一组图集之间的对应关系已知并传播到目标图像； (2) 在没有先验知识的情况下估计一组图像的对应关系。合成数据的评估表明，通过融合方法恢复的对应关系比基于总体模板注册的对应关系更准确。在真实 MRI 数据的 2D 示例中，融合方法会导致海马体手动分割之间的映射更加一致。
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. Effective label fusion strategies, such as local similarity-weighted voting 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.