  Deformable image registration is one of the fundamental tasks in medical
imaging. Classical registration algorithms usually require a high computational
cost for iterative optimizations. Although deep-learning-based methods have
been developed for fast image registration, it is still challenging to obtain
realistic continuous deformations from a moving image to a fixed image with
less topological folding problem. To address this, here we present a novel
diffusion-model-based image registration method, called DiffuseMorph.
DiffuseMorph not only generates synthetic deformed images through reverse
diffusion but also allows image registration by deformation fields.
Specifically, the deformation fields are generated by the conditional score
function of the deformation between the moving and fixed images, so that the
registration can be performed from continuous deformation by simply scaling the
latent feature of the score. Experimental results on 2D facial and 3D medical
image registration tasks demonstrate that our method provides flexible
deformations with topology preservation capability.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning