Deformer: Towards Displacement Field Learning for Unsupervised Medical Image Registration

TL;DR

This paper introduces Deformer, a novel module with a multi-scale framework that improves unsupervised deformable medical image registration by better modeling spatial relationships and displacement fields.

Contribution

The paper proposes a new Deformer module and a multi-scale framework that enhance displacement field learning for unsupervised medical image registration.

Findings

Outperforms traditional registration methods.

Effective in capturing spatial correspondence.

Validated on two public datasets.

Abstract

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.