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D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment

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Shape in Medical Imaging (ShapeMI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12474))

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Abstract

Alignment of contrast and non contrast-enhanced imaging is essential for quantification of changes in several biomedical applications. In particular, the extraction of cartilage shape from contrast-enhanced Computed Tomography (CT) of tibiae requires accurate alignment of the bone, currently performed manually. Existing deep learning-based methods for alignment require a common template or are limited in rotation range. Therefore, we present a novel network, D-net, to estimate arbitrary rotation and translation between 3D CT scans that additionally does not require a prior template. D-net is an extension to the branched Siamese encoder-decoder structure connected by new mutual, non-local links, which efficiently capture long-range connections of similar features between two branches. The 3D supervised network is trained and validated using preclinical CT scans of mouse tibiae with and without contrast enhancement in cartilage. The presented results show a significant improvement in the estimation of CT alignment, outperforming the current comparable methods.

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Acknowledgement

This work was supported by a Kennedy Trust for Rheumatology Research Studentship, the Centre for OA Pathogenesis Versus Arthritits (Versus Arthritis grant 21621). The authors acknowledge Patricia das Neves Borges as the researcher who collected the preclinical CT dataset, as part of the National Centre for Replacement, Refinement and Reduction of Animals in Research (NC3R grant NC/M000141/1). B. W. Papież acknowledges Rutherford Fund at Health Data Research UK

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Authors and Affiliations

  1. Kennedy Institute of Rheumatology, Nuffield Department of Rheumatology, Orthapaedics and Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Jian-Qing Zheng & Ngee Han Lim

  2. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    Bartłomiej W. Papież

Authors
  1. Jian-Qing Zheng
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  2. Ngee Han Lim
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  3. Bartłomiej W. Papież
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Corresponding author

Correspondence to Jian-Qing Zheng .

Editor information

Editors and Affiliations

  1. Harvard Medical School, Boston, USA

    Martin Reuter

  2. Klinikum der Universität München, LMU München, München, Germany

    Christian Wachinger

  3. Inria Sophia-Antipolis, Valbonne, France

    Hervé Lombaert

  4. University of North Carolina at Chapel Hill, Kitware Inc., Carrboro, NC, USA

    Beatriz Paniagua

  5. ETH Zurich, Zürich, Switzerland

    Orcun Goksel

  6. Istanbul Technical University, Istanbul, Turkey

    Islem Rekik

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Zheng, JQ., Lim, N.H., Papież, B.W. (2020). D-net: Siamese Based Network for Arbitrarily Oriented Volume Alignment. In: Reuter, M., Wachinger, C., Lombaert, H., Paniagua, B., Goksel, O., Rekik, I. (eds) Shape in Medical Imaging. ShapeMI 2020. Lecture Notes in Computer Science(), vol 12474. Springer, Cham. https://doi.org/10.1007/978-3-030-61056-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-61056-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61055-5

  • Online ISBN: 978-3-030-61056-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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