  Deformable medical image registration plays an important role in clinical
diagnosis and treatment. Recently, the deep learning (DL) based image
registration methods have been widely investigated and showed excellent
performance in computational speed. However, these methods cannot provide
enough registration accuracy because of insufficient ability in representing
both the global and local features of the moving and fixed images. To address
this issue, this paper has proposed the transformer based image registration
method. This method uses the distinctive transformer to extract the global and
local image features for generating the deformation fields, based on which the
registered image is produced in an unsupervised way. Our method can improve the
registration accuracy effectively by means of self-attention mechanism and
bi-level information flow. Experimental results on such brain MR image datasets
as LPBA40 and OASIS-1 demonstrate that compared with several traditional and DL
based registration methods, our method provides higher registration accuracy in
terms of dice values.
