Learning a Model-Driven Variational Network for Deformable Image Registration

Jia X, Thorley A, Chen W, Qiu H, Shen L, Styles IB, Chang HJ, Leonardis A, De Marvao A, O'Regan DP, Rueckert D, Duan J (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 41

Pages Range: 199-212

Journal Issue: 1

DOI: 10.1109/TMI.2021.3108881

Abstract

Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.

Involved external institutions

University of Birmingham GB United Kingdom (GB) Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) Shenzhen University (SZU) / 深圳大学 CN China (CN)

How to cite

APA:

Jia, X., Thorley, A., Chen, W., Qiu, H., Shen, L., Styles, I.B.,... Duan, J. (2022). Learning a Model-Driven Variational Network for Deformable Image Registration. IEEE Transactions on Medical Imaging, 41(1), 199-212. https://doi.org/10.1109/TMI.2021.3108881

MLA:

Jia, Xi, et al. "Learning a Model-Driven Variational Network for Deformable Image Registration." IEEE Transactions on Medical Imaging 41.1 (2022): 199-212.

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