DrivenMorph: 通过差异建模桥接注意力机制与变分图像配准
DrivenMorph: Bridging Attention Mechanism and Variational Image Registration via Difference Modeling
AI总结：
提出DrivenMorph框架，通过差异建模作为物理启发归纳偏置，结合注意力机制与变分图像配准，利用神经Demons层模拟力-位移交互生成平滑形变，在3D脑MRI数据集上优于现有方法。
摘要：
医学图像配准显著受益于深度学习，但现有方法往往缺乏物理解释性和细粒度变形控制。受Demons算法启发，我们提出一种新颖的DrivenMorph框架，通过将差异建模作为物理启发的归纳偏置，桥接注意力机制与变分图像配准。从潜在特征空间中的局部差异计算得到的驱动力，在整个配准过程中提供明确的语义指导。它通过一个神经Demons层直接驱动配准过程，该层模拟力-位移相互作用以生成平滑且解剖学一致的变形。与先前方法不同，我们的方法不仅将传统配准原理与流行的深度网络相结合，为基于学习的医学图像配准提供了可解释且高效的解决方案，而且将差异建模与变形分离，提高了模块化和可解释性。在多个3D脑MRI数据集上的大量实验表明，其性能优于最先进的基于学习和基于优化的方法。此外，可视化和统计分析证实，学习到的驱动力与实际变形模式紧密对齐，支持其解释价值。
英文摘要：
Medical image registration benefits significantly from deep learning, yet existing approaches often lack physical explainability and fine-grained deformation control. Motivated by Demons algorithms, we propose a novel DrivenMorph framework that bridges attention mechanisms with variational image registration by incorporating difference modeling as a physically inspired inductive bias. The resulting driving force, computed from local differences in the latent feature space, provides explicit semantic guidance throughout the registration process. It directly drives the registration process through a neural Demons layer that simulates force-displacement interactions to generate smooth and anatomically consistent deformation. Unlike previous methods, our approach not only integrates traditional registration principles with popular deep networks, providing an explainable and efficient solution for learning-based medical image registration, but also separates difference modeling from deformation, improving modularity and explainability. Extensive experiments on multiple 3D brain MRI datasets demonstrate superior performance over state of-the-art learning-based and optimization-based methods. Furthermore, visualizations and statistical analyses confirm that the learned driving force aligns closely with actual deformation patterns, supporting its explanatory value.
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