Image registration is a fundamental building block for various applications in medical image analysis. To better explore the correlation between the fixed and moving images and improve registration performance, we propose a novel deep learning network, Co-Attention guided Registration Network (CAR-Net). CAR-Net employs a co-attention block to learn a new representation of the inputs, which drives the registration of the fixed and moving images. Experiments on UK Biobank cardiac cine-magnetic resonance image data demonstrate that CAR-Net obtains higher registration accuracy and smoother deformation fields than state-of-the-art unsupervised registration methods, while achieving comparable or better registration performance than corresponding weakly-supervised variants. In addition, our approach can provide critical structural information of the input fixed and moving images simultaneously in a completely unsupervised manner.
@article{arxiv.2106.06637,
title = {CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning},
author = {Xiang Chen and Yan Xia and Nishant Ravikumar and Alejandro F Frangi},
journal= {arXiv preprint arXiv:2106.06637},
year = {2021}
}