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Endoscopic Navigation in the Absence of CT Imaging

  • Ayushi Sinha
  • Xingtong Liu
  • Austin Reiter
  • Masaru Ishii
  • Gregory D. Hager
  • Russell H. Taylor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11073)

Abstract

Clinical examinations that involve endoscopic exploration of the nasal cavity and sinuses often do not have a reference image to provide structural context to the clinician. In this paper, we present a system for navigation during clinical endoscopic exploration in the absence of computed tomography (CT) scans by making use of shape statistics from past CT scans. Using a deformable registration algorithm along with dense reconstructions from video, we show that we are able to achieve submillimeter registrations in in-vivo clinical data and are able to assign confidence to these registrations using confidence criteria established using simulated data.

Notes

Acknowledgment

This work was funded by NIH R01-EB015530, NSF Graduate Research Fellowship Program, an Intuitive Surgical, Inc. fellowship, and JHU internal funds.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ayushi Sinha
    • 1
  • Xingtong Liu
    • 1
  • Austin Reiter
    • 1
  • Masaru Ishii
    • 2
  • Gregory D. Hager
    • 1
  • Russell H. Taylor
    • 1
  1. 1.The Johns Hopkins UniversityBaltimoreUSA
  2. 2.Johns Hopkins Medical InstitutionsBaltimoreUSA

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