Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Temporal groupwise registration for motion modeling
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Robust measurement of individual localized changes to the aging hippocampus
Computer Vision and Image Understanding
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Accurate measurement of longitudinal changes of anatomical structure is important and challenging in many clinical studies. Also, for identification of disease-affected regions due to the brain disease, it is extremely necessary to register a population data to the common space simultaneously. In this paper, we propose a new method for simultaneous longitudinal and groupwise registration of a set of longitudinal data acquired from multiple subjects. Our goal is to 1) consistently measure the longitudinal changes from a sequence of longitudinal data acquired from the same subject; and 2) jointly align all image data (acquired from all time points of all subjects) to a hidden common space. To achieve these two goals, we first introduce a set of temporal fiber bundles to explore the spatial-temporal behavior of anatomical changes in each longitudinal data of the same subject. Then, a probabilistic model is built upon the hidden state of spatial smoothness and temporal continuity on the fibers. Finally, the transformation fields that connect each time-point image of each subject to the common space are simultaneously estimated by the expectation maximization (EM) approach, via the maximum a posterior (MAP) estimation of probabilistic models. Promising results are obtained to quantitatively measure the longitudinal changes of hippocampus volume, indicating better performance of our method than the conventional pairwise methods.