  We present deformable unsupervised medical image registration using a
randomly-initialized deep convolutional neural network (CNN) as regularization
prior. Conventional registration methods predict a transformation by minimizing
dissimilarities between an image pair. The minimization is usually regularized
with manually engineered priors, which limits the potential of the
registration. By learning transformation priors from a large dataset, CNNs have
achieved great success in deformable registration. However, learned methods are
restricted to domain-specific data and the required amounts of medical data are
difficult to obtain. Our approach uses the idea of deep image priors to combine
convolutional networks with conventional registration methods based on manually
engineered priors. The proposed method is applied to brain MRI scans. We show
that our approach registers image pairs with state-of-the-art accuracy by
providing dense, pixel-wise correspondence maps. It does not rely on prior
training and is therefore not limited to a specific image domain.
