Sep 17, 2026 · cs.CVJ/K move · Enter open · S save
Stefano Cerri, Amirhossein Hassankhani, Yaël Balbastre, Koen Van Leemput
Copenhagen Research Centre for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Denmark, and the Pioneer Centre for Artificial Intelligence, University of Copenhagen, Denmark · Department of Neuroscience and Biomedical Engineering, Aalto University, Finland · Department of Experimental Psychology, Division of Psychology and Language Sciences, University College London · Departments of Neuroscience and Biomedical Engineering and of Computer Science at Aalto University, Finland
We propose a new probabilistic model for general-purpose medical image registration that builds upon the mutual information registration criterion. It centers around a spatial interpolation technique that assumes latent voxel-wise correspondences between the images being registered. By exploiting these latent variables, we derive dedicated optimization and MCMC sampling techniques that only involve closed-form iterative updates. When applied to nonlinear registration, an efficient demons-like optimization algorithm is obtained that shows robust out-of-the-box performance across a variety of monomodal and multimodal registration tasks. We also demonstrate a corresponding sampler that can quantify, for the first time, uncertainty in multimodal registration scenarios with very high-dimensional 3D deformations. Our code, which we call BINDER (Bayesian INference for DEformable Registration), is freely available at https://github.com/ste93ste/BINDER.