CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning

• URL: http://arxiv.org/abs/2106.06637v1
• Date: Fri, 11 Jun 2021 23:25:49 GMT
• Title: CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning
• Authors: Xiang Chen, Yan Xia, Nishant Ravikumar, Alejandro F Frangi
• Abstract summary: 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.
• Score: 73.03885837923599
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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