Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set.
Hierarchical unbiased graph shrinkage (HUGS): a novel groupwise registration for large data set.
复制标题
分层无偏图收缩（HUGS）：一种新颖的大数据集分组配准
DOI：
10.1016/j.neuroimage.2013.09.023
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发表时间：
2014-01-01
期刊：
影响因子：
5.7
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Ying S;Wu G;Wang Q;Shen D
将大型数据集中的所有图像归一化到公共空间中是许多临床和研究中的关键步骤，例如，促进大脑发育成熟和衰老最近，已经开发了分组配准，用于在不选择特定图像作为模板的情况下同时对准所有图像，从而潜在地避免配准中的偏差。然而，大多数传统的groupwise配准方法不探索的数据分布在图像配准。因此，其性能可能会受到注册数据集中受试者间较大差异的影响。为了解决这个潜在的问题，我们建议使用一个图来模拟图像流形上所有图像数据的分布，每个节点代表一个图像，每个边代表两个节点（或图像）之间的测地线路径。然后，将所有图像扭曲到它们的群体中心的过程变成图节点沿着它们的图边缘的动态收缩，直到所有图节点变得彼此接近。因此，在分组配准期间，图像流形上的图像分布的拓扑结构始终被保持。更重要的是，通过对所有图像的分布进行建模，我们可以潜在地减少配准误差，因为每次每个图像都只根据图中具有相似结构的邻近图像进行扭曲。我们已经评估了我们提出的groupwise注册方法的婴儿和成人数据集，也比较与传统的组平均值为基础的注册和ABSORB方法。实验结果表明，该方法在配准精度和鲁棒性方面都有较好的性能。
Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness.
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DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
DOI：
10.1007/11566489_51
发表时间：
2005-01-01
期刊：
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2005, PT 2
影响因子：
--
作者：
Lorenzen, P;Davis, B;Joshi, S
通讯作者：
Joshi, S
影响因子：
10.6
作者：
Rohlfing T
通讯作者：
Rohlfing T
影响因子：
5.7
作者：
Fonov V;Evans AC;Botteron K;Almli CR;McKinstry RC;Collins DL;Brain Development Cooperative Group
通讯作者：
Brain Development Cooperative Group
影响因子：
10.6
作者：
Auzias, Guillaume;Colliot, Olivier;Baillet, Sylvain
通讯作者：
Baillet, Sylvain