Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion_Journal of Biomedical Engineering

Authors：

SHEN Yu ,  YAN Yuan , SONG Jing , LIU Guanghui , XU Jiawen , WEI Ziyi

School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, P. R. China;

Corresponding author：

YAN Yuan, Email: 17613451281@163.com

Keywords：

Medical image processing; Parallel path; Multi-scale fusion; Lightweight model

DOI：

10.7507/1001-5515.202309014

Video：

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Abstract Full text Figures/Tables Video References Cited by

Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.

Citation： SHEN Yu, YAN Yuan, SONG Jing, LIU Guanghui, XU Jiawen, WEI Ziyi. Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion. Journal of Biomedical Engineering, 2024, 41(2): 213-219. doi: 10.7507/1001-5515.202309014 Copy

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14.	Martino A D, Yan C G, Li Q, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatr, 2014, 19(6): 659-667.
15.	Alessa H, Lasse H, Mok T, et al. Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Trans Med Imaging, 2022, 42(3): 697-712.
16.	Avants B B, Epstein C L, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal, 2008, 12(1): 26-41.

1. 马露凡, 罗凤, 严江鹏, 等. 深度医学图像配准研究进展: 迈向无监督学习. 中国图象图形学报, 2021, 26(9): 2037-2057.
2. Li Y, Tang H, Wang W, et al. Dual attention network for unsupervised medical image registration based on VoxelMorph. Sci Rep, 2022, 12(1): 16250.
3. Liu Y, Wang W, Li Y, et al. Geometry-consistent adversarial registration model for unsupervised multi-modal medical image registration. IEEE J Biomed Health Inform, 2023, 27(7): 3455-3466.
4. Deng L, Zhi Q, Huang S, et al. A deformable patch-based transformer for 3D medical image registration. Int J Comput Assist Radiol Surg, 2023, 18(12): 2295-2306.
5. Tang K, Wang L, Huang X, et al. MD-SGT: Multi-dilation spherical graph transformer for unsupervised medical image registration. Comput Med Imaging Graph, 2023, 108: 102281.
6. Balakrishnan G, Zhao A, Sabuncu M R, et al. VoxelMorph: A learning framework for deformable medical image registration. IEEE Trans Med Imaging, 2019, 38(8): 1788-1800.
7. Mok T C W, Chung A C S. Fast symmetric diffeomorphic image registration with convolutional neural networks// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE, 2020: 4644-4653.
8. Mingrui M, Yuanbo X, Lei S, et al. Symmetric transformer-based network for unsupervised image registration. Knowl-Based Syst, 2022, 257: 109959.
9. Chen J, Frey E, He Y, et al. TransMorph: Transformer for unsupervised medical image registration. Med Image Anal, 2022, 82: 102615.
10. Max J, Karen S, Andrew Z, et al. Spatial transformer networks. ArXiv, 2015, 28: 02025.
11. Ding X, Zhang X, Zhou Y, et al. Scaling up your kernels to 31×31: Revisiting large kernel design in CNNs// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022: 11953-11965.
12. Jia X, Bartlett J, Zhang T, et al. U-Net vs transformer: Is U-Net outdated in medical image registration?// Lian C, Cao X, Rekik I, et al. Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science. Cham: Springer, 2022, 13583: 151-160.
13. Marcus D S, Wang T H, Parker J, et al. Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cognit Neurosci, 2014, 19(9): 1498-1507.
14. Martino A D, Yan C G, Li Q, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatr, 2014, 19(6): 659-667.
15. Alessa H, Lasse H, Mok T, et al. Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. IEEE Trans Med Imaging, 2022, 42(3): 697-712.
16. Avants B B, Epstein C L, Grossman M, et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal, 2008, 12(1): 26-41.

Journal of Biomedical Engineering

Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion

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