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International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis

International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention

MICCAI Challenge on Correction of Brainshift with Intra-Operative Ultrasound

LABELS 2019, HAL-MICCAI 2019, CuRIOUS 2019: Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention pp 136–144Cite as

Landmark-Based Evaluation of a Block-Matching Registration Framework on the RESECT Pre- and Intra-operative Brain Image Data Set

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11851))

Abstract

In this paper, we describe the application of an established block-matching based registration method to the CuRIOUS 2019 MICCAI registration challenge. Directional and symmetric approaches with different parameters are evaluated to select the most suitable setting of this fully automatic and general registration method. The results can be used as a baseline, for example when evaluating methods specialised in ultrasound (US) to MRI registration or registration of different interventional US (iUS) data. This work is a continuation of our contribution to the CuRIOUS 2018 challenge. We provide a more extensive analysis of main parameters as well as add pre- to post-resection iUS registration to the previous MRI-iUS registration. The proposed approach achieves an average target registration error of 2.68 mm and 1.92 mm for the MR-iUS and the iUS-iUS task respectively.

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References

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Acknowledgments

This work is supported by the UCL EPSRC Centre for Doctoral Training in Medical Imaging [EP/L016478/1], the Wellcome/EPSRC Centre for Interventional and Surgical Sciences [NS/A000050/1], the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z] and EPSRC [NS/A000027/1]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Quadro P6000 used for this research. This research was supported by the NIHR BRC based at GSTT and KCL.

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Authors and Affiliations

  1. Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, WC1E 6BT, UK

    David Drobny

  2. Medical Physics and Biomedical Engineering Department, University College London, London, WC1E 6BT, UK

    Marta Ranzini

  3. School of Biomedical Engineering & Imaging Sciences, King’s College London, King’s Health Partners, St Thomas’ Hospital, London, SE1 7EH, UK

    David Drobny, Marta Ranzini, Sébastien Ourselin, Tom Vercauteren & Marc Modat

Authors
  1. David Drobny
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  2. Marta Ranzini
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  3. Sébastien Ourselin
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  4. Tom Vercauteren
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  5. Marc Modat
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Corresponding author

Correspondence to David Drobny .

Editor information

Editors and Affiliations

  1. University of Sydney, Sydney, NSW, Australia

    Luping Zhou

  2. University of Minnesota, Minneapolis, MN, USA

    Nicholas Heller

  3. University of Notre Dame, Notre Dame, IN, USA

    Yiyu Shi

  4. Western University, London, ON, Canada

    Yiming Xiao

  5. University of Bern, Bern, Switzerland

    Raphael Sznitman

  6. Eindhoven University of Technology, Eindhoven, The Netherlands

    Veronika Cheplygina

  7. École Centrale de Nantes, Nantes, France

    Diana Mateus

  8. University of Dundee, Dundee, UK

    Emanuele Trucco

  9. University of Notre Dame, Notre Dame, IN, USA

    X. Sharon Hu

  10. University of Notre Dame, Notre Dame, IN, USA

    Danny Chen

  11. University of Grenoble Alpes, Grenoble, France

    Matthieu Chabanas

  12. Concordia University, Montréal, QC, Canada

    Hassan Rivaz

  13. Health Research, SINTEF Digital, Trondheim, Norway

    Ingerid Reinertsen

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Drobny, D., Ranzini, M., Ourselin, S., Vercauteren, T., Modat, M. (2019). Landmark-Based Evaluation of a Block-Matching Registration Framework on the RESECT Pre- and Intra-operative Brain Image Data Set. In: Zhou, L., et al. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention. LABELS HAL-MICCAI CuRIOUS 2019 2019 2019. Lecture Notes in Computer Science(), vol 11851. Springer, Cham. https://doi.org/10.1007/978-3-030-33642-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-33642-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33641-7

  • Online ISBN: 978-3-030-33642-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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