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Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces

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Information Processing in Medical Imaging (IPMI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12729))

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Abstract

High screening coverage during colonoscopy is crucial to effectively prevent colon cancer. Previous work has allowed alerting the doctor to unsurveyed regions by reconstructing the 3D colonoscopic surface from colonoscopy videos in real-time. However, the lighting inconsistency of colonoscopy videos can cause a key component of the colonoscopic reconstruction system, the SLAM optimization, to fail. In this work we focus on the lighting problem in colonoscopy videos. To successfully improve the lighting consistency of colonoscopy videos, we have found necessary a lighting correction that adapts to the intensity distribution of recent video frames. To achieve this in real-time, we have designed and trained an RNN network. This network adapts the gamma value in a gamma-correction process. Applied in the colonoscopic surface reconstruction system, our light-weight model significantly boosts the reconstruction success rate, making a larger proportion of colonoscopy video segments reconstructable and improving the reconstruction quality of the already reconstructed segments.

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Notes

  1. 1.

    We implemented the training in [6] without color constancy loss using the colonoscopy images from our training set.

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Acknowledgement

We thank Prof. Jan-Michael Frahm for useful consultations. This work was carried out with financial support from the Olympus Corporation, the UNC Kenan Professorship Fund, and the UNC Lineberger Cancer Center.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Yubo Zhang, Shuxian Wang, Ruibin Ma & Stephen M. Pizer

  2. Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Sarah K. McGill

  3. Department of Radiation Oncology, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Julian G. Rosenman

Authors
  1. Yubo Zhang
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  2. Shuxian Wang
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  3. Ruibin Ma
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  4. Sarah K. McGill
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  5. Julian G. Rosenman
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  6. Stephen M. Pizer
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Corresponding author

Correspondence to Yubo Zhang .

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Editors and Affiliations

  1. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  2. University of Copenhagen, Copenhagen, Denmark

    Stefan Sommer

  3. King's College London, London, UK

    Julia Schnabel

  4. University of Copenhagen, Copenhagen, Denmark

    Mads Nielsen

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Zhang, Y., Wang, S., Ma, R., McGill, S.K., Rosenman, J.G., Pizer, S.M. (2021). Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds) Information Processing in Medical Imaging. IPMI 2021. Lecture Notes in Computer Science(), vol 12729. Springer, Cham. https://doi.org/10.1007/978-3-030-78191-0_43

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  • DOI: https://doi.org/10.1007/978-3-030-78191-0_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78190-3

  • Online ISBN: 978-3-030-78191-0

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