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ManiSMC: A New Method Using Manifold Modeling and Sequential Monte Carlo Sampler for Boosting Navigated Bronchoscopy

  • Xiongbiao Luo
  • Takayuki Kitasaka
  • Kensaku Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

This paper presents a new bronchoscope motion tracking method that utilizes manifold modeling and sequential Monte Carlo (SMC) sampler to boost navigated bronchoscopy. Our strategy to estimate the bronchoscope motions comprises two main stages:(1) bronchoscopic scene identification and (2) SMC sampling. We extend a spatial local and global regressive mapping (LGRM) method to Spatial-LGRM to learn bronchoscopic video sequences and construct their manifolds. By these manifolds, we can classify bronchoscopic scenes to bronchial branches where a bronchoscope is located. Next, we employ a SMC sampler based on a selective image similarity measure to integrate estimates of stage (1) to refine positions and orientations of a bronchoscope. Our proposed method was validated on patient datasets. Experimental results demonstrate the effectiveness and robustness of our method for bronchoscopic navigation without an additional position sensor.

Keywords

Training Image Sequential Monte Carlo Virtual Camera Airway Tree Manifold Modeling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Xiongbiao Luo
    • 1
  • Takayuki Kitasaka
    • 2
  • Kensaku Mori
    • 3
    • 1
  1. 1.Graduate School of Information ScienceNagoya UniversityJapan
  2. 2.Faculty of Information ScienceAichi Institute of TechnologyJapan
  3. 3.Information and Communications HeadquartersNagoya UniversityJapan

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