Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification

© Springer Nature Switzerland AG 2018. This paper presents a novel approach to modeling the posterior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric mapping (LDDMM). We develop a Laplace approximation of Bayesian registration models en...

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Bibliographic Details
Main Authors: Wang, Jian (Author), Wells, William M. (Author), Golland, Polina (Author), Zhang, Miaomiao (Author)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor)
Format: Article
Language:English
Published: Springer International Publishing, 2021-11-22T20:13:50Z.
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