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arXiv · 2609.19875

BINDER: A Latent Variable Model for Probabilistic Medical Image Registration

Stefano Cerri·Amirhossein Hassankhani·Yaël Balbastre·Koen Van Leemput
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Abstract

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.

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Stefano Cerri, Amirhossein Hassankhani, Yaël Balbastre, Koen Van Leemput. 2026-09-17. BINDER: A Latent Variable Model for Probabilistic Medical Image Registration. https://arxiv.org/abs/2609.19875

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