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Large Deformation Image Registration with Anatomy-Aware Laplacian Pyramid Networks

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Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12587))

Abstract

Deep learning-based methods have recently demonstrated remarkable results in deformable image registration for a wide range of medical image analysis tasks. However, most of the deep learning-based approaches are often limited to small deformation settings. In this paper, we describe a deformable image registration approach for the Learn2Reg 2020 challenge based on the Laplacian pyramid image registration networks. Our approach won 1st place in the Learn2Reg 2020 challenge.

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Author information

Authors and Affiliations

  1. Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Tony C. W. Mok & Albert C. S. Chung

Authors
  1. Tony C. W. Mok
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  2. Albert C. S. Chung
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Corresponding author

Correspondence to Tony C. W. Mok .

Editor information

Editors and Affiliations

  1. Massachusetts General Hospital, Boston, MA, USA

    Nadya Shusharina

  2. Universität zu Lübeck, Lübeck, Germany

    Mattias P. Heinrich

  3. Shenzhen University, Shenzhen, China

    Ruobing Huang

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Mok, T.C.W., Chung, A.C.S. (2021). Large Deformation Image Registration with Anatomy-Aware Laplacian Pyramid Networks. In: Shusharina, N., Heinrich, M.P., Huang, R. (eds) Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data. MICCAI 2020. Lecture Notes in Computer Science(), vol 12587. Springer, Cham. https://doi.org/10.1007/978-3-030-71827-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-71827-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71826-8

  • Online ISBN: 978-3-030-71827-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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