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Workshop on Clinical Image-Based Procedures

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

CLIP 2019, UNSURE 2019: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures pp 115–124Cite as

Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11840))

Abstract

Between preoperative computed tomography (CT) image acquisition and endoscopic sinus surgery, the nasal cavity of a patient undergoes changes. These changes make it challenging for non-deformable vision-based registration algorithms to find accurate alignments between CT image and intraoperative video. Large alignment errors can lead to injuries to critical structures. In this paper, we present a deformable video-CT registration that deforms the patient shape extracted from CT according to statistics learned from population. We also associate confidence with regions of deformed shapes based on the location of matched video features. Experiments on both simulation and in vivo data produced < 1 mm errors (statistically significantly lower than prior work).

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Notes

  1. 1.

    All statistical significance figures reported in this paper are evaluated using the paired-sample Student’s t-test and indicate \(p<0.001\).

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Acknowledgment

This work was funded by the Johns Hopkins University (JHU) Provost’s Postdoctoral Fellowship and other JHU internal funds. We would also like to thank Seth D. Billings for his invaluable feedback.

Author information

Authors and Affiliations

  1. The Johns Hopkins University, Baltimore, USA

    Ayushi Sinha, Xingtong Liu, Gregory D. Hager & Russell H. Taylor

  2. Johns Hopkins Medical Institutions, Baltimore, USA

    Masaru Ishii

Authors
  1. Ayushi Sinha
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  2. Xingtong Liu
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  3. Masaru Ishii
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  4. Gregory D. Hager
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  5. Russell H. Taylor
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Corresponding author

Correspondence to Ayushi Sinha .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. University College London, London, UK

    Ryutaro Tanno

  3. Fraunhofer Singapore, Nanyang Technological University, Singapore, Singapore

    Marius Erdt

  4. McGill University, Montreal, QC, Canada

    Tal Arbel

  5. ETH Zürich, Zürich, Switzerland

    Christian Baumgartner

  6. Massachusetts Institute of Technology, Harvard Medical School, Cambridge, MA, USA

    Adrian Dalca

  7. University College London, King's College London, London, UK

    Carole H. Sudre

  8. Harvard Medical School, Boston, MA, USA

    William M. Wells

  9. Aachen University of Applied Sciences, Aachen, Germany

    Klaus Drechsler

  10. Children’s National Healthcare System, Washington, D.C., DC, USA

    Marius George Linguraru

  11. Fraunhofer IGD, Darmstadt, Germany

    Cristina Oyarzun Laura

  12. Children's National Healthcare System, Washington, D.C., DC, USA

    Raj Shekhar

  13. Fraunhofer IGD, Darmstadt, Germany

    Stefan Wesarg

  14. ICREA - Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

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Sinha, A., Liu, X., Ishii, M., Hager, G.D., Taylor, R.H. (2019). Recovering Physiological Changes in Nasal Anatomy with Confidence Estimates. In: Greenspan, H., et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures. CLIP UNSURE 2019 2019. Lecture Notes in Computer Science(), vol 11840. Springer, Cham. https://doi.org/10.1007/978-3-030-32689-0_12

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  • DOI: https://doi.org/10.1007/978-3-030-32689-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32688-3

  • Online ISBN: 978-3-030-32689-0

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