Unsupervised similarity learning for image registration with energy-based models
Author(s)
Type
Conference Paper
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.
Date Issued
2024-10-05
Date Acceptance
2024-10-01
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024, 15249, pp.229-240
ISBN
9783031734793
ISSN
0302-9743
Publisher
Springer
Start Page
229
End Page
240
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
15249
Copyright Statement
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
11th International Workshop, WBIR 2024, in Conjunction with MICCAI 2024
Publication Status
Published
Start Date
2024-10-06
Coverage Spatial
Marrakesh, Morocco
Date Publish Online
2024-10-05