A variational Bayesian method for similarity learning in non-rigid image registration

Grzech D, Azampour MF, Glocker B, Schnabel J, Navab N, Kainz B, Folgoc LL (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition IEEE Computer Society; 1999

Publisher: IEEE Computer Society

Book Volume: 2022-June

Pages Range: 119-128

Conference Proceedings Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Event location: New Orleans, LA US

ISBN: 9781665469463

DOI: 10.1109/CVPR52688.2022.00022

Abstract

We propose a novel variational Bayesian formulation for diffeomorphic non-rigid registration of medical images, which learns in an unsupervised way a data-specific similarity metric. The proposed framework is general and may be used together with many existing image registration models. We evaluate it on brain MRI scans from the UK Biobank and show that use of the learnt similarity metric, which is parametrised as a neural network, leads to more accurate results than use of traditional functions, e.g. SSD and LCC, to which we initialise the model, without a negative impact on image registration speed or transformation smoothness. In addition, the method estimates the uncertainty associated with the transformation. The code and the trained models are available in a public repository: https://github.com/dgrzech/learnsim.

Authors with CRIS profile

Bernhard Kainz Professur für Image Data Exploration and Analysis

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Grzech, D., Azampour, M.F., Glocker, B., Schnabel, J., Navab, N., Kainz, B., & Folgoc, L.L. (2022). A variational Bayesian method for similarity learning in non-rigid image registration. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 119-128). New Orleans, LA, US: IEEE Computer Society.

MLA:

Grzech, Daniel, et al. "A variational Bayesian method for similarity learning in non-rigid image registration." Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA IEEE Computer Society, 2022. 119-128.

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