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Updated: Dec 31, 2025

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ManiSMC: a new method using manifold modeling and sequential Monte Carlo sampler for boosting navigated bronchoscopy.

Xiongbiao Luo1, Takayuki Kitasaka, Kensaku Mori

  • 1Graduate School of Information Science, Nagoya University, Japan.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 19, 2011
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This study introduces a novel bronchoscope motion tracking method using manifold modeling and sequential Monte Carlo (SMC) sampling to improve navigated bronchoscopy accuracy without extra sensors.

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Area of Science:

  • Medical Imaging
  • Robotics
  • Computer Vision

Background:

  • Navigated bronchoscopy requires precise tracking of the bronchoscope's position and orientation.
  • Existing methods may rely on additional sensors, increasing complexity and cost.

Purpose of the Study:

  • To develop and validate a novel, sensor-free method for accurate bronchoscope motion tracking.
  • To enhance the precision and robustness of navigated bronchoscopy procedures.

Main Methods:

  • Bronchoscopic scene identification using an extended Spatial Local and Global Regressive Mapping (Spatial-LGRM) to construct scene manifolds.
  • Sequential Monte Carlo (SMC) sampling integrated with a selective image similarity measure for refining bronchoscope pose estimation.
  • Validation on patient datasets to assess performance.

Main Results:

  • The proposed method effectively classifies bronchoscopic scenes to identify bronchial branches.
  • SMC sampling refines bronchoscope position and orientation estimates with high accuracy.
  • Experimental results demonstrate the method's effectiveness and robustness in real-world patient data.

Conclusions:

  • The novel manifold modeling and SMC sampling approach offers a robust, sensor-free solution for bronchoscope motion tracking.
  • This advancement has the potential to improve the safety and efficacy of navigated bronchoscopy.