  Recently, deep-learning-based approaches have been widely studied for
deformable image registration task. However, most efforts directly map the
composite image representation to spatial transformation through the
convolutional neural network, ignoring its limited ability to capture spatial
correspondence. On the other hand, Transformer can better characterize the
spatial relationship with attention mechanism, its long-range dependency may be
harmful to the registration task, where voxels with too large distances are
unlikely to be corresponding pairs. In this study, we propose a novel Deformer
module along with a multi-scale framework for the deformable image registration
task. The Deformer module is designed to facilitate the mapping from image
representation to spatial transformation by formulating the displacement vector
prediction as the weighted summation of several bases. With the multi-scale
framework to predict the displacement fields in a coarse-to-fine manner,
superior performance can be achieved compared with traditional and
learning-based approaches. Comprehensive experiments on two public datasets are
conducted to demonstrate the effectiveness of the proposed Deformer module as
well as the multi-scale framework.
