eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration.

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PloS one, ISSN: 1932-6203, Vol: 11, Issue: 1, Page: e0146870

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10.1371/journal.pone.0146870; 10.1371/journal.pone.0146870.g002; 10.1371/journal.pone.0146870.g012; 10.1371/journal.pone.0146870.g010; 10.1371/journal.pone.0146870.g011; 10.1371/journal.pone.0146870.g001; 10.1371/journal.pone.0146870.g004; 10.1371/journal.pone.0146870.g006; 10.1371/journal.pone.0146870.g008; 10.1371/journal.pone.0146870.g003; 10.1371/journal.pone.0146870.g005; 10.1371/journal.pone.0146870.g009; 10.1371/journal.pone.0146870.g007
PMC4723358; 4723358
Guorong Wu; Xuewei Peng; Shihui Ying; Qian Wang; Pew-Thian Yap; Dan Shen; Dinggang Shen; Yuanquan Wang
Public Library of Science (PLoS); Figshare
Biochemistry, Genetics and Molecular Biology; Agricultural and Biological Sciences; Imaging techniques; Cerebrospinal fluid; Clustering algorithms; Neuroimaging; Elderly; Geodesics; Manifolds; Deformation; Biological Sciences; groupwise registration methods; image distribution; graph; image distribution manifold; novel groupwise registration method; Efficient Groupwise Registration; image distribution information; Enhanced Hierarchical Unbiased Graph Shrinkage; Uncategorised; Physical Sciences and Mathematics; Statistics and Probability
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Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results.