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

Luo, Xiangfeng; Kitasaka, Takayuki; Mori, Kensaku

doi:10.1007/978-3-642-23626-6_31

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Feature generation (Chien et al, 2020;Byrnes and Higgins, 2014;? ;Luó et al, 2012a) and similarity measures (Luo and Mori, 2014;Shen et al, 2015;Luo et al, 2011;Khare and Higgins, 2010) were commonly used for such purpose, accounting, however, with tracking errors and large execution times.…”

Section: Methodsmentioning

confidence: 99%

BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation

Borrego-Carazo¹,

Sánchez²,

Castells-Rufas³

et al. 2022

Preprint

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Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the videobronchoscopy to provide an effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera, or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.In the present paper we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standartized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state of the art camera pose estimation in videobronchoscopy.

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