Acta Optica Sinica, Volume. 44, Issue 19, 1915002(2024)

Design of Large Deformable Lung Image Registration Network with Adaptive Window

Jianbing Yi1,2、*, Xi Chen1,2, Feng Cao1,2, Shuxin Yang1,2、**, and Xin Chen1,2
Author Affiliations
  • 1School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi , China
  • 2Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control, Ganzhou 341000, Jiangxi , China
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    References(29)

    [3] Feng J, Deng J H, Zhou M et al. Review of multimodal medical image registration algorithm[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 52, 29-49, 157(2024).

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    [15] Chang Q, Wang Y Q, Zhang J M. Independently trained multi-scale registration network based on image pyramid[J]. Journal of Imaging Informatics in Medicine, 37, 1557-1566(2024).

    [17] Zhou H C, Leung H, Balaji B. AR-UNet: a deformable image registration network with cyclic training[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 21, 692-700(2024).

    [21] Zhang H, Zu K K, Lu J et al. EPSANet: an efficient pyramid squeeze attention block on convolutional neural network[M]. Computer vision‒ACCV, 13843, 541-557(2022).

    [23] Yi J B[M]. Robust point matching for lung 4D-CT, 22-24(2019).

    [29] Lin L H, Yi J B, Cao F et al. Non-rigid registration algorithm of lung computed tomography image based on multi-scale parallel fully convolutional neural network[J]. Laser & Optoelectronics Progress, 59, 1617004(2022).

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    Jianbing Yi, Xi Chen, Feng Cao, Shuxin Yang, Xin Chen. Design of Large Deformable Lung Image Registration Network with Adaptive Window[J]. Acta Optica Sinica, 2024, 44(19): 1915002

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    Paper Information

    Category: Machine Vision

    Received: Mar. 27, 2024

    Accepted: May. 20, 2024

    Published Online: Oct. 12, 2024

    The Author Email: Yi Jianbing (yijianbing8@jxust.edu.cn), Yang Shuxin (yangshuxin@jxust.edu.cn)

    DOI:10.3788/AOS240778

    CSTR:32393.14.AOS240778

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