We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.

翻译：我们提出KeyMorph，一种基于深度学习的图像配准框架，其核心依赖于自动检测对应关键点。当前最先进的深度学习方法在配准中通常对大偏移不鲁棒、缺乏可解释性，且未融入问题的对称性。此外，大多数模型在测试时仅产生单一预测结果。针对这些不足，我们的核心洞察在于：图像间的对应关键点可通过可微分的闭式表达式获取最优变换。我们利用这一观察驱动面向配准任务的关键点端到端学习，且无需已知真实关键点。该框架不仅显著提升了配准的鲁棒性，还增强了可解释性——关键点揭示了驱动最终对齐的图像区域。此外，KeyMorph可被设计为对图像平移具有等变性，且/或对输入图像顺序具有对称性。最后，我们展示了如何在测试时高效地以闭式形式计算对应于不同变换变体的多个形变场。我们在多模态脑部MRI扫描的3D仿射和样条配准任务中验证了所提框架。实验表明，其配准精度超越了当前最先进方法，尤其在大位移场景中表现突出。我们的代码开源在 https://github.com/alanqrwang/keymorph。