  Deformable image registration can obtain dynamic information about images,
which is of great significance in medical image analysis. The unsupervised deep
learning registration method can quickly achieve high registration accuracy
without labels. However, these methods generally suffer from uncorrelated
features, poor ability to register large deformations and details, and
unnatural deformation fields. To address the issues above, we propose an
unsupervised multi-scale correlation iterative registration network
(SearchMorph). In the proposed network, we introduce a correlation layer to
strengthen the relevance between features and construct a correlation pyramid
to provide multi-scale relevance information for the network. We also design a
deformation field iterator, which improves the ability of the model to register
details and large deformations through the search module and GRU while ensuring
that the deformation field is realistic. We use single-temporal brain MR images
and multi-temporal echocardiographic sequences to evaluate the model's ability
to register large deformations and details. The experimental results
demonstrate that the method in this paper achieves the highest registration
accuracy and the lowest folding point ratio using a short elapsed time to
state-of-the-art.
