发表机构
Copenhagen University Hospital; University of Copenhagen; Aalto University; University College London（哥本哈根大学医院; 哥本哈根大学; 阿尔托大学; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出基于互信息准则和潜变量空间插值的概率配准模型BINDER，推导闭式迭代优化与MCMC采样，实现稳健的非线性配准及多模态高维形变不确定性量化。
AI 中文摘要
我们提出了一种新的通用医学图像配准概率模型，该模型基于互信息配准准则构建。其核心是一种空间插值技术，该技术假设待配准图像之间存在体素级的潜变量对应关系。通过利用这些潜变量，我们推导出仅涉及闭式迭代更新的专用优化和MCMC采样技术。当应用于非线性配准时，我们获得了一种高效的类demons优化算法，该算法在多种单模态和多模态配准任务中展现出稳健的开箱即用性能。我们还展示了相应的采样器，该采样器首次能够量化具有非常高维3D形变的多模态配准场景中的不确定性。我们的代码名为BINDER（贝叶斯推断用于可变形配准），可从此https URL免费获取。
英文摘要
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.
