  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.
