TY - GEN
T1 - Unsupervised Similarity Learning for Image Registration with Energy-Based Models
AU - Grzech, Daniel
AU - Folgoc, Loïc Le
AU - Azampour, Mohammad Farid
AU - Vlontzos, Athanasios
AU - Glocker, Ben
AU - Navab, Nassir
AU - Schnabel, Julia
AU - Kainz, Bernhard
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - energy-based models
KW - image registration
UR - http://www.scopus.com/inward/record.url?scp=85206883961&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73480-9_18
DO - 10.1007/978-3-031-73480-9_18
M3 - Conference contribution
AN - SCOPUS:85206883961
SN - 9783031734793
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 240
BT - Biomedical Image Registration - 11th International Workshop, WBIR 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Modat, Marc
A2 - Špiclin, Žiga
A2 - Hering, Alessa
A2 - Simpson, Ivor
A2 - Bastiaansen, Wietske
A2 - Mok, Tony C. W.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 6 October 2024 through 6 October 2024
ER -