title: ManiSMC: a new method using manifold modeling and sequential Monte Carlo sampler for boosting navigated bronchoscopy.
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.
authors: Graduate School of Information Science, Nagoya University, Japan.
authors: Xiongbiao
authors: X
authors: Luo
authors: Takayuki
authors: T
authors: Kitasaka
authors: Kensaku
authors: K
authors: Mori
mesh_terms: D000465:Algorithms; D020680:Bronchoscopes; D001999:Bronchoscopy / Q000379:methods*; D016000:Cluster Analysis; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009010:Monte Carlo Method; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods; D014741:Video Recording