Medical image registration drives quantitative analysis across organs, modalities, and patient populations. Recent deep learning methods often combine low-level "trend-driven" computational blocks from computer vision, such as large-kernel CNNs, Transformers, and state-space models, with high-level registration-specific designs like motion pyramids, correlation layers, and iterative refinement. Yet, their relative contributions remain unclear and entangled. This raises a central question: should future advances in registration focus on importing generic architectural trends or on refining domain-specific design principles? Through a modular framework spanning brain, lung, cardiac, and abdominal registration, we systematically disentangle the influence of these two paradigms. Our evaluation reveals that low-level "trend-driven" computational blocks offer only marginal or inconsistent gains, while high-level registration-specific designs consistently deliver more accurate, smoother, and more robust deformations. These domain priors significantly elevate the performance of a standard U-Net baseline, far more than variants incorporating "trend-driven" blocks, achieving an average relative improvement of $\sim3\%$. All models and experiments are released within a transparent, modular benchmark that enables plug-and-play comparison for new architectures and registration tasks (https://github.com/BailiangJ/rethink-reg). This dynamic and extensible platform establishes a common ground for reproducible and fair evaluation, inviting the community to isolate genuine methodological contributions from domain priors. Our findings advocate a shift in research emphasis: from following architectural trends to embracing domain-specific design principles as the true drivers of progress in learning-based medical image registration.

翻译：医学图像配准推动了跨器官、模态和患者群体的定量分析。近期的深度学习方法通常将计算机视觉中的低层“趋势驱动”计算模块（如大核CNN、Transformer和状态空间模型）与高层配准特定设计（如运动金字塔、相关层和迭代优化）相结合。然而，它们的相对贡献仍不明确且相互纠缠。这引发了一个核心问题：配准的未来进展应侧重于引入通用架构趋势，还是应精炼领域特定的设计原则？通过一个涵盖脑部、肺部、心脏和腹部配准的模块化框架，我们系统性地解构了这两种范式的影响。我们的评估表明，低层“趋势驱动”计算模块仅带来边际或不一致的增益，而高层配准特定设计则持续提供更准确、更平滑且更稳健的形变。这些领域先验显著提升了标准U-Net基线的性能，远超融合“趋势驱动”模块的变体，实现了平均约3%的相对改进。所有模型和实验均在一个透明、模块化的基准框架中发布，支持新架构和配准任务的即插即用比较（https://github.com/BailiangJ/rethink-reg）。这一动态可扩展平台为可复现和公平评估建立了共同基础，邀请研究界从领域先验中分离出真正的方法学贡献。我们的研究主张转变研究重点：从追随架构趋势转向拥抱领域特定设计原则，将其作为基于学习的医学图像配准进展的真正驱动力。