A novel unsupervised learning model for diffeomorphic image registration

Yongpei Zhu; Zicong Zhou; Guojun Liao; Kehong Yuan

doi:10.1117/12.2580815

15 February 2021 A novel unsupervised learning model for diffeomorphic image registration

Yongpei Zhu, Zicong Zhou, Guojun Liao, Kehong Yuan

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115960M (2021) https://doi.org/10.1117/12.2580815
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Deformable medical image registration is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a novel unsupervised learning model (denoted as BSADM) for 3D diffeomorphic medical image registration. Inspired by spatial attention module, we propose a new network architecture BSAU-Net by introducing a novel Binary Spatial Attention Module (BSAM) into skip connection, which can take full advantages of the spatial information extracted from the encoding path and corresponding decoding path. In addition, from variational method in differential geometry, monitor function f is used to control the Jacobian determinant (JD) of registration field ɸ. So, we also propose a novel orientation-consistent regularization loss to penalize the local regions with negative Jacobian determinant, which further encourages the diffeomorphic property of the transformations. We verify our method on two datasets including ADNI and PPMI dataset, and obtain excellent improvement on magnetic resonance (MR) image registration with higher average Dice scores and better diffeomorphic registration.

Conference Presentation

Citation Download Citation

Yongpei Zhu, Zicong Zhou, Guojun Liao, and Kehong Yuan "A novel unsupervised learning model for diffeomorphic image registration", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115960M (15 February 2021); https://doi.org/10.1117/12.2580815

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