L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration-Reference-Cited by-同舟云学术

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L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration

Published:2024-01-10 Issue:3 Volume:14 Page:497-509
ISSN:2093-9868
Container-title:Biomedical Engineering Letters
language:en
Short-container-title:Biomed. Eng. Lett.

Author:

Deng Liwei^ORCID,Zou Yanchao,Yang Xin^ORCID,Wang Jing,Huang Sijuan

Funder

National Science Foundation for Young Scientists of China

Natural Science Foundation of Heilongjiang Province

Key R&D Program Guidance Projects of Heilongjiang Province

Basic and Applied Basic Research Foundation of Guangdong Province

Youth Innovation Project of Sun Yat-sen University Cancer Center

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13534-023-00344-1.pdf

Reference42 articles.

1. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. An unsupervised learning model for deformable medical image registration. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. p. 9252–60. https://doi.org/10.1109/CVPR.2018.00964.

2. Zhu F, Wang S, Li D, Li Q. Similarity attention-based CNN for robust 3D medical image registration. Biomed Signal Process Control. 2023;81:104403. https://doi.org/10.1016/j.bspc.2022.104403.

3. Huang J, Guo J, Pedrosa I, Fei B. Deep learning-based deformable registration of dynamic contrast-enhanced MR images of the kidney. Proc SPIE Int Soc Opt Eng. 2022;12034:213. https://doi.org/10.1117/12.2611768.

4. Shi HB, Lu LY, Yin MX, Zhong C, Yang F. Joint few-shot registration and segmentation self-training of 3D medical images. Biomed Signal Process Control. 2023;80:104294. https://doi.org/10.1016/j.bspc.2022.104294.

5. Zheng ZY, Cao WM, Duan Y, Cao GT, Lian DL. Multi-strategy mutual learning network for deformable medical image registration. Neurocomputing. 2022;501:102–12. https://doi.org/10.1016/j.neucom.2022.06.020.

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