Grzech D, Azampour MF, Glocker B, Schnabel J, Navab N, Kainz B, Folgoc LL (2022)
Publication Type: Conference contribution
Publication year: 2022
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
ISBN: 9781665469463
DOI: 10.1109/CVPR52688.2022.00022
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.
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.
BibTeX: Download