BINDER: A Latent Variable Model for Probabilistic Medical Image Registration

2026-09-17

AI Summary

提出了一种基于互信息标准的概率医学图像配准模型BINDER，通过假设潜在体素对应关系，使用优化和MCMC采样技术解决非线性和多模态图像配准问题。

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.

Problem

• medical image registration
• probabilistic model
• latent variable
• uncertainty quantification
• nonlinear registration

Innovation

• probabilistic model
• latent variables
• optimization and MCMC sampling
• demons-like optimization algorithm
• uncertainty quantification