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Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge

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Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis (MICCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13166))

Abstract

Hyperparameter tuning is extremely tedious and costly in the deep learning-based deformable image registration methods. In this paper, we present our contribution to the Learn2Reg challenge and demonstrate how hyperparameter tuning can be accelerated and simplified with the proposed conditional image registration framework. We exemplify the conditional image registration framework with the deep Laplacian pyramid image registration network (cLapIRN) and apply it comprehensively to all three tasks in the challenge. Our method was ranked the first place in the Learn2Reg 2021 challenge.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Sai Kung, 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. Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Marc Aubreville

  2. German Cancer Research Center (DKFZ), Heidelberg, Germany

    David Zimmerer

  3. Universität zu Lübeck, Lübeck, Germany

    Mattias Heinrich

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Mok, T.C.W., Chung, A.C.S. (2022). Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge. In: Aubreville, M., Zimmerer, D., Heinrich, M. (eds) Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. MICCAI 2021. Lecture Notes in Computer Science(), vol 13166. Springer, Cham. https://doi.org/10.1007/978-3-030-97281-3_23

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  • DOI: https://doi.org/10.1007/978-3-030-97281-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-97280-6

  • Online ISBN: 978-3-030-97281-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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