ModeT: Learning Deformable Image Registration via Motion Decomposition
  Transformer
Haiqiao Wang Dong Ni Yi Wang
The Transformer structures have been widely used in computer vision and have
recently made an impact in the area of medical image registration. However, the
use of Transformer in most registration networks is straightforward. These
networks often merely use the attention mechanism to boost the feature learning
as the segmentation networks do, but do not sufficiently design to be adapted
for the registration task. In this paper, we propose a novel motion
decomposition Transformer (ModeT) to explicitly model multiple motion
modalities by fully exploiting the intrinsic capability of the Transformer
structure for deformation estimation. The proposed ModeT naturally transforms
the multi-head neighborhood attention relationship into the multi-coordinate
relationship to model multiple motion modes. Then the competitive weighting
module (CWM) fuses multiple deformation sub-fields to generate the resulting
deformation field. Extensive experiments on two public brain magnetic resonance
imaging (MRI) datasets show that our method outperforms current
state-of-the-art registration networks and Transformers, demonstrating the
potential of our ModeT for the challenging non-rigid deformation estimation
problem. The benchmarks and our code are publicly available at
https://github.com/ZAX130/SmileCode.