Symmetric Transformer-based Network for Unsupervised Image Registration

TL;DR

This paper introduces SymTrans, a symmetric transformer-based neural network with a novel efficient attention mechanism for unsupervised medical image registration, achieving state-of-the-art results.

Contribution

It proposes a convolution-based efficient multi-head self-attention (CEMSA) and a symmetric transformer architecture for improved registration performance.

Findings

Achieves state-of-the-art registration accuracy

Reduces model parameters and computational complexity

Effective in displacement field and diffeomorphic registration

Abstract

Medical image registration is a fundamental and critical task in medical image analysis. With the rapid development of deep learning, convolutional neural networks (CNN) have dominated the medical image registration field. Due to the disadvantage of the local receptive field of CNN, some recent registration methods have focused on using transformers for non-local registration. However, the standard Transformer has a vast number of parameters and high computational complexity, which causes Transformer can only be applied at the bottom of the registration models. As a result, only coarse information is available at the lowest resolution, limiting the contribution of Transformer in their models. To address these challenges, we propose a convolution-based efficient multi-head self-attention (CEMSA) block, which reduces the parameters of the traditional Transformer and captures local spatial context information for reducing semantic ambiguity in the attention mechanism. Based on the proposed CEMSA, we present a novel Symmetric Transformer-based model (SymTrans). SymTrans employs the Transformer blocks in the encoder and the decoder respectively to model the long-range spatial cross-image relevance. We apply SymTrans to the displacement field and diffeomorphic registration. Experimental results show that our proposed method achieves state-of-the-art performance in image registration. Our code is publicly available at \url{https://github.com/MingR-Ma/SymTrans}.