MSRA-Net: multi-channel semantic-aware and residual attention mechanism network for unsupervised 3D image registration

Abstract

Objective: Convolutional neural network (CNN) is developing rapidly in the field of medical image registration, and the proposed U-Net further improves the precision of registration. However, this method may discard certain important information in the process of encoding and decoding steps, consequently leading to a decline in accuracy. To solve this problem, a multi-channel semantic-aware and residual attention mechanism network (MSRA-Net) is proposed in this paper. 
Approach: Our proposed network achieves efficient information aggregation by cleverly extracting the features of different channels. Firstly, a context-aware module (CAM) is designed to extract valuable contextual information. And the depth-wise separable convolution is employed in the CAM to alleviate the computational burden. Then, a new multi-channel semantic-aware module (MCSAM) is designed for more comprehensive fusion of up-sampling features. Additionally, the residual attention module (RAM) is introduced in the up-sampling process to extract more semantic information and minimize information loss.
Main results: This study utilizes Dice score, ASSD and negative Jacobian determinant evaluation metrics to evaluate the influence of registration. The experimental results demonstrate that our proposed MSRA-Net has the highest accuracy compared to several state-of-the-art methods. Moreover, our network has demonstrated the highest Dice score across multiple datasets, thereby indicating that the superior generalization capabilities of our model.
Significance: The proposed MSRA-Net offers a novel approach to improve medical image registration accuracy, with implications for various clinical applications.Our implementation is available at https://github.com/shy922/MSRA-Net.