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Bildverarbeitung für die Medizin 2021 pp 125–126Cite as

Abstract: Probabilistic Dense Displacement Networks for Medical Image Registration

Contributions to the Learn2Reg Challenge

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Part of the book series: Informatik aktuell ((INFORMAT))

Abstract

Medical image registration plays a vital role in various clinical workflows, diagnosis, research studies and computer-assisted interventions. Currently, deep learning based registration methods are starting to show promising improvements that could advance the accuracy, robustness and computation speed of conventional algorithms. However, until recently there was no commonly used benchmark dataset available to compare learning approaches with each other and their conventional (not trained) counterparts.

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References

  1. Hansen L, Hering A, Heinrich M, et al.. Learn2Reg: 2020 MICCAI registration challenge; 2020. https://learn2reg.grand-challenge.org.

  2. Heinrich MP. Closing the gap between deep and conventional image registration using probabilistic dense displacement networks. Proc MICCAI. 2019; p. 50–58.

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  3. Heinrich MP, Hansen L. Highly accurate and memory efficient unsupervised learning-based discrete CT registration using 2.5 D displacement search. Proc MICCAI. 2020; p. 190200. github.com/multimodallearning/pdd2.5.

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Author information

Authors and Affiliations

  1. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Deutschland

    Lasse Hansen & Mattias P. Heinrich

Authors
  1. Lasse Hansen
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  2. Mattias P. Heinrich
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Corresponding author

Correspondence to Lasse Hansen .

Editor information

Editors and Affiliations

  1. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

    Christoph Palm

  2. Peter L. Reichertz Institut für Medizinische Informatik der TU Braunschweig und der Medizinischen Hochschule Hannover, Braunschweig, Germany

    Thomas M. Deserno

  3. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

    Heinz Handels

  4. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

    Andreas Maier

  5. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Klaus Maier-Hein

  6. Institut für Medizinische Informatik, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Thomas Tolxdorff

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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Hansen, L., Heinrich, M.P. (2021). Abstract: Probabilistic Dense Displacement Networks for Medical Image Registration. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_30

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