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
this https URL

本文提出一种新颖的运动分解 Transformer (ModeT), 用于像医学图像配准中的非刚性变形估计，出色地超越了当前最先进的配准网络和 Transformer, 并展示了我们的 ModeT 的潜力。