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A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences



A novel framework for longitudinal atlas construction with groupwise registration of subject image sequences



Neuroimage 59(2): 1275-1289



Longitudinal atlas construction plays an important role in medical image analysis. Given a set of longitudinal images from different subjects, the task of longitudinal atlas construction is to build an atlas sequence which can represent the trend of anatomical changes of the population. The major challenge for longitudinal atlas construction is how to effectively incorporate both the subject-specific information and population information to build the unbiased atlases. In this paper, a novel groupwise longitudinal atlas construction framework is proposed to address this challenge, and the main contributions of the proposed framework lie in the following aspects: (1) The subject-specific longitudinal information is captured by building the growth model for each subject. (2) The longitudinal atlas sequence is constructed by performing groupwise registration among all the subject image sequences, and only one transformation is needed to transform each subject's image sequence to the atlas space. The constructed longitudinal atlases are unbiased and no explicit template is assumed. (3) The proposed method is general, where the number of longitudinal images of each subject and the time points at which they are taken can be different. The proposed method is extensively evaluated on two longitudinal databases, namely the BLSA and ADNI databases, to construct the longitudinal atlas sequence. It is also compared with a state-of-the-art longitudinal atlas construction algorithm based on kernel regression on the temporal domain. Experimental results demonstrate that the proposed method consistently achieves higher registration accuracies and more consistent spatial-temporal correspondences than the compared method on both databases.

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Accession: 036185233

Download citation: RISBibTeXText

PMID: 21884801

DOI: 10.1016/j.neuroimage.2011.07.095


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