Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
Name
nihms-1026606.pdf
Description
Accepted version
Size
816.97 KB
Format
Unknown
Checksum (MD5)
ae0eb3b57761737e34bd20fc74424038
Author(s)
Wang, Jian
Wells, William M.
Golland, Polina
Zhang, Miaomiao
Date Issued
2018
Publisher
Springer International Publishing
Citation
Wang, Jian, Wells, William M., Golland, Polina and Zhang, Miaomiao. 2018. "Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification."
Version
Author's final manuscript
Abstract
© 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 entirely in a bandlimited space that fully describes the properties of diffeomorphic transformations. In contrast to current methods, we compute the inverse Hessian at the mode of the posterior distribution of diffeomorphisms directly in the low dimensional frequency domain. This dramatically reduces the computational complexity of approximating posterior marginals in the high dimensional imaging space. Experimental results show that our method is significantly faster than the state-of-the-art diffeomorphic image registration uncertainty quantification algorithms, while producing comparable results. The efficiency of our method strengthens the feasibility in prospective clinical applications, e.g., real-time image-guided navigation for brain surgery.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-00928-1_99
