Unsupervised Similarity Learning for Image Registration with Energy-Based Models

Grzech D, Folgoc LL, Azampour MF, Vlontzos A, Glocker B, Navab N, Schnabel J, Kainz B (2024)

Publication Type: Conference contribution

Publication year: 2024

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 15249 LNCS

Pages Range: 229-240

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Marrakesh, MAR

ISBN: 9783031734793

DOI: 10.1007/978-3-031-73480-9_18

Open Access Link: https://link.springer.com/chapter/10.1007/978-3-031-73480-9_18

Abstract

We present a new model for deformable image registration, which learns in an unsupervised way a data-specific similarity metric. The proposed method consists of two neural networks, one that maps pairs of input images to transformations which align them, and one that provides the similarity metric whose maximisation guides the image alignment. We parametrise the similarity metric as an energy-based model, which is simple to train and allows us to improve the accuracy of image registration compared to other models with learnt similarity metrics by taking advantage of a more general mathematical formulation, as well as larger datasets. We also achieve substantial improvement in the accuracy of inter-patient image registration on MRI scans from the OASIS dataset compared to models that rely on traditional functions.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) LMU Klinikum DE Germany (DE)

How to cite

APA:

Grzech, D., Folgoc, L.L., Azampour, M.F., Vlontzos, A., Glocker, B., Navab, N.,... Kainz, B. (2024). Unsupervised Similarity Learning for Image Registration with Energy-Based Models. In Marc Modat, Žiga Špiclin, Alessa Hering, Ivor Simpson, Wietske Bastiaansen, Tony C. W. Mok (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 229-240). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.

MLA:

Grzech, Daniel, et al. "Unsupervised Similarity Learning for Image Registration with Energy-Based Models." Proceedings of the 11th International Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Marc Modat, Žiga Špiclin, Alessa Hering, Ivor Simpson, Wietske Bastiaansen, Tony C. W. Mok, Springer Science and Business Media Deutschland GmbH, 2024. 229-240.

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