SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
No Thumbnail Available
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Honkamaa, Joel
Marttinen, Pekka
Date
2024-11
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
47
Series
The Journal of Machine Learning for Biomedical Imaging, Volume 2, pp. 2148-2194
Abstract
Deep learning has emerged as a strong alternative for classical iterative methods for de- formable medical image registration, where the goal is to find a mapping between the coordinate systems of two images. Popular classical image registration methods enforce the useful inductive biases of symmetricity, inverse consistency, and topology preservation by construction. However, while many deep learning registration methods encourage these properties via loss functions, no earlier methods enforce all of them by construction. Here, we propose a novel registration architecture based on extracting multi-resolution feature representations which is by construction symmetric, inverse consistent, and topology pre- serving. We also develop an implicit layer for memory efficient inversion of the deformation fields. Our method achieves state-of-the-art registration accuracy on three datasets. The code is available at https://github.com/honkamj/SITRegDescription
| openaire: EC/H2020/101016775/EU//INTERVENE
Keywords
Other note
Citation
Honkamaa, J & Marttinen, P 2024, ' SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration ', The Journal of Machine Learning for Biomedical Imaging, vol. 2, 026, pp. 2148-2194 . https://doi.org/10.59275:j.melba.2024-276b