eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

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Wu, Guorong
- Affiliation: School of Medicine, Department of Radiology
Peng, Xuewei
- Affiliation: School of Medicine, Department of Radiology
- Other Affiliation: Department of Electrical and Computer Engineering; Texas AandM University
Ying, Shihui
- Other Affiliation: Department of Mathematics; School of Science; Shanghai University
Wang, Qian
- Other Affiliation: Med-X Research Institute; Shanghai Jiao Tong University
Yap, Pew-Thian
- Affiliation: School of Medicine, Department of Radiology
Shen, Dan
- Other Affiliation: Department of Mathematics and Statistics; University of South Florida
Shen, Dinggang
- Affiliation: School of Medicine, Department of Radiology
- Other Affiliation: Department of Brain and Cognitive Engineering; Korea University

Abstract

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.

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