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International Workshop on Reconstruction and Analysis of Moving Body Organs

International Workshop on Breast Image Analysis

International Workshop on Thoracic Image Analysis

RAMBO 2018, BIA 2018, TIA 2018: Image Analysis for Moving Organ, Breast, and Thoracic Images pp 272–283Cite as

Rigid Lens – Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scans

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11040)

Abstract

A general problem of any deformable image registration method for change assessment is to find a good balance between computing a precise match and keeping locally differences. In this work we present the rigid lens concept dealing with this issue. The rigid lens is based on locally rigid approximation of locally precise deformations and can be used for interactive viewing and visualization of changes as well as for automatic change detection. We demonstrate the rigid lens in the context of oncological workup of thorax-abdomen CT follow-up scans and evaluate the concept for change assessment based on a study with 1492 manually annotated lesion in scans from more than 400 patients.

Keywords

  • Change assessment
  • Image registration
  • Local rigidity

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Acknowledgment

This research was supported by the AMI (Automation in Medical Imaging) project under the ICON program of the Fraunhofer Society, Germany. We gratefully acknowledge Bram van Ginneken and Colin Jacobs from the Diagnostic Image Analysis Group of Radboud University Medical Center, Nijmegen, the Netherlands for providing us data and for their value input in joint discussions.

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Medical Image Computing MEVIS, Lübeck, Germany

    Sonja Jäckle & Stefan Heldmann

Authors
  1. Sonja Jäckle
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  2. Stefan Heldmann
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Corresponding author

Correspondence to Sonja Jäckle .

Editor information

Editors and Affiliations

  1. University College London, London, UK

    Danail Stoyanov

  2. University of Leeds, Leeds, UK

    Dr. Zeike Taylor

  3. Imperial College London, London, UK

    Bernhard Kainz

  4. University of Adelaide, Adelaide, SA, Australia

    Dr. Gabriel Maicas

  5. University of Iowa, Iowa City, IA, USA

    Reinhard R. Beichel

  6. Sunnybrook Health Science Centre, Toronto, ON, Canada

    Anne Martel

  7. Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Lena Maier-Hein

  8. Visulytix Ltd. Screenworks, London, UK

    Kanwal Bhatia

  9. King’s College London, London, UK

    Tom Vercauteren

  10. Imperial College London, London, UK

    Ozan Oktay

  11. University of Adelaide, Adelaide, SA, Australia

    Prof. Gustavo Carneiro

  12. Queensland University of Technology, Brisbane, QLD, Australia

    Andrew P. Bradley

  13. University of Lisbon, Lisbon, Portugal

    Prof. Jacinto Nascimento

  14. University of Queensland, Brisbane, QLD, Australia

    Hang Min

  15. University of California Los Angeles, Los Angeles, CA, USA

    Matthew S. Brown

  16. Radboud University Medical Center, Nijmegen, The Netherlands

    Dr. Colin Jacobs

  17. Fraunhofer Institute for Medical Image Computing (MEVIS), Bremen, Germany

    Bianca Lassen-Schmidt

  18. Nagoya University, Nagoya, Japan

    Prof. Kensaku Mori

  19. University of Copenhagen, Copenhagen, Denmark

    Jens Petersen

  20. Harvard Medical School, Boston, MA, USA

    Raúl San José Estépar

  21. Philips (Germany), Hamburg, Germany

    Alexander Schmidt-Richberg

  22. University College London, London, UK

    Catarina Veiga

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Jäckle, S., Heldmann, S. (2018). Rigid Lens – Locally Rigid Approximations of Deformable Registration for Change Assessment in Thorax-Abdomen CT Follow-Up Scans. In: , et al. Image Analysis for Moving Organ, Breast, and Thoracic Images. RAMBO BIA TIA 2018 2018 2018. Lecture Notes in Computer Science(), vol 11040. Springer, Cham. https://doi.org/10.1007/978-3-030-00946-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-00946-5_27

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