eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration

PloS One
Guorong WuDinggang Shen

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...Continue Reading

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Citations

Jul 22, 2019·Medical & Biological Engineering & Computing·Jianjun ZhuJian Yang

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Methods Mentioned

BETA
PCA

Software Mentioned

ABSORB
ABSOB
ABSORB ( Building by Self Registration Bundling )
Brain Surface Extractor ( BSE )
FLIRT
FAST
HUGS
FSL
Brain Extraction Tool ( BET )
eHUGS

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