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. © 2011 Springer-Verlag.
CITATION STYLE
Luo, X., Kitasaka, T., & Mori, K. (2011). ManiSMC: A new method using manifold modeling and sequential monte carlo sampler for boosting navigated bronchoscopy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6893 LNCS, pp. 248–255). https://doi.org/10.1007/978-3-642-23626-6_31
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