可解释的可变形图像配准：几何深度学习的视角
Interpretable deformable image registration: A geometric deep learning perspective
AI总结：
本文从几何深度学习视角提出可解释的可变形图像配准框架，通过分离特征提取与形变建模、动态感受野和空间连续形变函数实现由粗到精的配准，并在脑部与视网膜配准任务上显著超越现有方法。
摘要：
可变形图像配准提出了一个具有挑战性的问题，与大多数深度学习任务不同，它必须考虑多个坐标系之间的复杂关系。尽管数据驱动方法在建模复杂非线性变换方面已展现出有前景的能力，但现有工作采用标准深度学习架构，假定它们是通用的黑盒求解器。我们认为，理解学习到的操作如何在源域和目标域的特征之间执行模式匹配，是构建鲁棒、数据高效且可解释架构的关键。我们提出了设计可解释配准框架的理论基础：分离的特征提取与形变建模、动态感受野，以及数据驱动的形变函数对两个空间域之间关系的感知。基于这一基础，我们制定了一个端到端过程，以由粗到精的方式细化变换。我们的架构采用空间连续的形变建模函数，运用几何深度学习原理，从而避免了在连续细化变换之间重采样到规则网格这一有问题的方法。我们进行了定性研究，以突出我们架构中有趣的可解释性特性。最后，我们展示了在单模态和多模态个体间脑部配准以及纵向视网膜个体内配准这一具有挑战性的任务上，性能指标相较于最先进方法有显著提升。我们已将代码公开。
英文摘要：
Deformable image registration poses a challenging problem where, unlike most deep learning tasks, a complex relationship between multiple coordinate systems has to be considered. Although data-driven methods have shown promising capabilities to model complex non-linear transformations, existing works employ standard deep learning architectures assuming they are general black-box solvers. We argue that understanding how learned operations perform pattern-matching between the features in the source and target domains is the key to building robust, data-efficient, and interpretable architectures. We present a theoretical foundation for designing an interpretable registration framework: separated feature extraction and deformation modeling, dynamic receptive fields, and a data-driven deformation functions awareness of the relationship between both spatial domains. Based on this foundation, we formulate an end-to-end process that refines transformations in a coarse-to-fine fashion. Our architecture employs spatially continuous deformation modeling functions that use geometric deep-learning principles, therefore avoiding the problematic approach of resampling to a regular grid between successive refinements of the transformation. We perform a qualitative investigation to highlight interesting interpretability properties of our architecture. We conclude by showing significant improvement in performance metrics over state-of-the-art approaches for both mono- and multi-modal inter-subject brain registration, as well as the challenging task of longitudinal retinal intra-subject registration. We make our code publicly available
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