  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.
