Chen, X orcid.org/0000-0003-4203-4578, Xia, Y, Ravikumar, N et al. (1 more author) (2021) CAR-Net: Unsupervised Co-Attention Guided Registration Network for Joint Registration and Structure Learning. [Preprint - arXiv]
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.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Keywords: | Medical Image Registration, Cardiac Image Registration, Co-Attention, Deep Learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 11 Dec 2024 16:21 |
Last Modified: | 11 Dec 2024 16:25 |
Published Version: | http://arxiv.org/abs/2106.06637v1 |
Identification Number: | 10.48550/arXiv.2106.06637 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176703 |