TransMorph: Transformer for unsupervised medical image registration

by   Junyu Chen, et al.

In the last decade, convolutional neural networks (ConvNets) have dominated the field of medical image analysis. However, it is found that the performances of ConvNets may still be limited by their inability to model long-range spatial relations between voxels in an image. Numerous vision Transformers have been proposed recently to address the shortcomings of ConvNets, demonstrating state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their self-attention mechanism enables a more precise comprehension of the spatial correspondence between moving and fixed images. In this paper, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. We also introduce three variants of TransMorph, with two diffeomorphic variants ensuring the topology-preserving deformations and a Bayesian variant producing a well-calibrated registration uncertainty estimate. The proposed models are extensively validated against a variety of existing registration methods and Transformer architectures using volumetric medical images from two applications: inter-patient brain MRI registration and phantom-to-CT registration. Qualitative and quantitative results demonstrate that TransMorph and its variants lead to a substantial performance improvement over the baseline methods, demonstrating the effectiveness of Transformers for medical image registration.


page 15

page 17

page 19

page 23

page 25

page 26

page 28

page 29


ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration

In the last decade, convolutional neural networks (ConvNets) have domina...

Symmetric Transformer-based Network for Unsupervised Image Registration

Medical image registration is a fundamental and critical task in medical...

Deformable Cross-Attention Transformer for Medical Image Registration

Transformers have recently shown promise for medical image applications,...

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

Dynamic medical imaging is usually limited in application due to the lar...

MoViT: Memorizing Vision Transformers for Medical Image Analysis

The synergy of long-range dependencies from transformers and local repre...

Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers

Image registration is an essential but challenging task in medical image...

RFR-WWANet: Weighted Window Attention-Based Recovery Feature Resolution Network for Unsupervised Image Registration

The Swin transformer has recently attracted attention in medical image a...

Please sign up or login with your details

Forgot password? Click here to reset