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International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging

International Workshop on Graphs in Biomedical Image Analysis

UNSURE 2020, GRAIL 2020: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis pp 3–12Cite as

Image Registration via Stochastic Gradient Markov Chain Monte Carlo

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12443))

Abstract

We develop a fully Bayesian framework for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images along with calibrated uncertainty estimates is difficult for both computational and modelling reasons. To address the computational issues, we explore connections between the Markov chain Monte Carlo by backprop and the variational inference by backprop frameworks in order to efficiently draw thousands of samples from the posterior distribution. Regarding the modelling issues, we carefully design a Bayesian model for registration to overcome the existing barriers when using a dense, high-dimensional, and diffeomorphic parameterisation of the transformation. This results in improved calibration of uncertainty estimates.

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Acknowledgments

This research was conducted using the UK Biobank resources under application number 12579. DG is funded by the EPSRC CDT for Smart Medical Imaging EP/S022104/1 and LlF by EP/P023509/1.

Author information

Authors and Affiliations

  1. Department of Computing, Imperial College London, London, UK

    Daniel Grzech, Bernhard Kainz, Ben Glocker & Loïc le Folgoc

Authors
  1. Daniel Grzech
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  2. Bernhard Kainz
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  3. Ben Glocker
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  4. Loïc le Folgoc
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Corresponding author

Correspondence to Daniel Grzech .

Editor information

Editors and Affiliations

  1. University College London, London, UK

    Carole H. Sudre

  2. University of Oxford, Oxford, UK

    Hamid Fehri

  3. McGill University, Montreal, QC, Canada

    Tal Arbel

  4. ETH Zurich, Zürich, Switzerland

    Christian F. Baumgartner

  5. Massachusetts General Hospital, Charlestown, MA, USA

    Adrian Dalca

  6. University College London, London, UK

    Ryutaro Tanno

  7. Technical University of Denmark, Kongens Lyngby, Denmark

    Koen Van Leemput

  8. Harvard Medical School, Boston, MA, USA

    William M. Wells

  9. Washington University School of Medicine, St. Louis, MO, USA

    Aristeidis Sotiras

  10. University of Oxford, Oxford, UK

    Bartlomiej Papiez

  11. Ciudad Universitaria UNL, Santa Fe, Argentina

    Enzo Ferrante

  12. Huawei Noah’s Ark Lab, London, UK

    Sarah Parisot

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Grzech, D., Kainz, B., Glocker, B., le Folgoc, L. (2020). Image Registration via Stochastic Gradient Markov Chain Monte Carlo. In: Sudre, C.H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. UNSURE GRAIL 2020 2020. Lecture Notes in Computer Science(), vol 12443. Springer, Cham. https://doi.org/10.1007/978-3-030-60365-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-60365-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60364-9

  • Online ISBN: 978-3-030-60365-6

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