Health AI Central
Peer-Reviewed Publication
Med Image Anal2026;108103854.February 1, 2026Journal Article

From model based to learned regularization in medical image registration: A comprehensive review.

Anna Reithmeir1, Veronika Spieker2, Vasiliki Sideri-Lampretsa3, Daniel Rueckert4, Julia A Schnabel5, Veronika A Zimmer6
1School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany; Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany. Electronic address: anna.reithmeir@tum.de.
2School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany.
3School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany. Electronic address: vasiliki.sideri-lampretsa@tum.de.
4School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany; Department of Computing, Imperial College London, London, United Kingdom; School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Munich, Germany. Electronic address: daniel.rueckert@tum.de.
5School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; Munich Center for Machine Learning (MCML), Munich, Germany; Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Munich, Germany; School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom. Electronic address: julia.schnabel@tum.de.
6School of Computation, Information and Technology, Technical University of Munich (TUM), Munich, Germany; School of Medicine and Health, TUM University Hospital, Technical University of Munich (TUM), Munich, Germany. Electronic address: veronika.zimmer@tum.de.
PubMed DOI

Abstract

Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration is to precisely capture the deformation between two or more images, typically achieved by minimizing an optimization problem. Due to its inherent ill-posedness, regularization is a key component in driving the solution to…

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