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...
Main Authors: | Wang, Jian (Author), Wells, William M. (Author), Golland, Polina (Author), Zhang, Miaomiao (Author) |
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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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Subjects: | |
Online Access: | Get fulltext |
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