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International Symposium on Visual Computing

ISVC 2019: Advances in Visual Computing pp 320–331Cite as

Benchmarking Video with the Surgical Image Registration Generator (SIRGn) Baseline

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

Abstract

Augmented Reality (AR) surgical image guidance overlays preoperative data into the surgeon’s view in real time during the procedure. Non-rigid 3D registration is a critical and often challenging step for AR surgical image guidance. Since surgical environments vary greatly and registration must by done quickly and accurately, it is unlikely that one registration technique will work well over different surgical scenarios. Unfortunately, it is currently challenging to evaluate the accuracy and effectiveness of 3D registration techniques on surgical scenes. In this work, we provide a novel method to benchmark quality of non-rigid 3D surface registration. Our method provides a triangular mesh overlay representing the quality of registration and can highlight areas of unacceptably poor registration performance given some specified tolerance. We use the method to evaluate the quality of two existing non-rigid registration approaches on surgical video.

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Notes

  1. 1.

    Example code and data available at: https://github.com/KastnerRG/SIRGn.git.

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Author information

Authors and Affiliations

  1. University of California, San Diego, La Jolla, CA, 92093, USA

    Michael Barrow, Peter Tueller & Ryan Kastner

  2. VMware, Palo Alto, CA, 94304, USA

    Nelson Ho

  3. Leidos, San Diego, CA, 92127, USA

    Alric Althoff

Authors
  1. Michael Barrow
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  2. Nelson Ho
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  3. Alric Althoff
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  4. Peter Tueller
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  5. Ryan Kastner
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Corresponding author

Correspondence to Michael Barrow .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Barrow, M., Ho, N., Althoff, A., Tueller, P., Kastner, R. (2019). Benchmarking Video with the Surgical Image Registration Generator (SIRGn) Baseline. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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