ManiSMC: a new method using manifold modeling and sequential monte carlo sampler for boosting navigated bronchoscopy

Authors:
Xiongbiao Luo;Takayuki Kitasaka;Kensaku Mori
Affiliations:
Graduate School of Information Science, Nagoya University, Japan;Faculty of Information Science, Aichi Institute of Technology, Japan;Information and Communications Headquarters and Graduate School of Information Science, Nagoya University, Japan
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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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.