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2018 | OriginalPaper | Chapter

Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification

Authors : Jian Wang, William M. Wells III, Polina Golland, Miaomiao Zhang

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps

To prevent the domain from growing infinity, we truncate the output of the convolution in each dimension to a suitable finite set.

For notation simplification, we define the time derivative \(\dot{\tilde{v}}_t \triangleq d \tilde{v}_t /dt\).

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Title: Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification
Authors: Jian Wang
William M. Wells III
Polina Golland
Miaomiao Zhang
Publisher: Springer International Publishing
Book: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Print ISBN: 978-3-030-00927-4

Electronic ISBN: 978-3-030-00928-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00928-1_99

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