Imperial College London
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DR BERNHARD KAINZ

Faculty of Engineering, Department of Computing

Reader in Medical Image Computing
 
 
 
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Contact

 

+44 (0)20 7594 8349b.kainz Website CV

 
 
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Location

 

372Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Grzech:2022:10.48550/arXiv.2110.13289,
author = {Grzech, D and Azampour, MF and Qiu, H and Glocker, B and Kainz, B and Folgoc, LL},
doi = {10.48550/arXiv.2110.13289},
publisher = {ArXiv},
title = {Uncertainty quantification in non-rigid image registration via  stochastic gradient Markov chain Monte Carlo},
url = {http://dx.doi.org/10.48550/arXiv.2110.13289},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - We develop a new Bayesian model for non-rigid registration ofthree-dimensional medical images, with a focus on uncertainty quantification.Probabilistic registration of large images with calibrated uncertaintyestimates is difficult for both computational and modelling reasons. To addressthe computational issues, we explore connections between the Markov chain MonteCarlo by backpropagation and the variational inference by backpropagationframeworks, in order to efficiently draw samples from the posteriordistribution of transformation parameters. To address the modelling issues, weformulate a Bayesian model for image registration that overcomes the existingbarriers when using a dense, high-dimensional, and diffeomorphic transformationparametrisation. This results in improved calibration of uncertainty estimates.We compare the model in terms of both image registration accuracy anduncertainty quantification to VoxelMorph, a state-of-the-art image registrationmodel based on deep learning.
AU  - Grzech,D
AU  - Azampour,MF
AU  - Qiu,H
AU  - Glocker,B
AU  - Kainz,B
AU  - Folgoc,LL
DO  - 10.48550/arXiv.2110.13289
PB  - ArXiv
PY  - 2022///
TI  - Uncertainty quantification in non-rigid image registration via  stochastic gradient Markov chain Monte Carlo
UR  - http://dx.doi.org/10.48550/arXiv.2110.13289
UR  - http://arxiv.org/abs/2110.13289v1
UR  - http://hdl.handle.net/10044/1/97323
ER  -
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