Automated Anatomical Labeling of Bronchial Branches Extracted from CT Datasets Based on Machine Learning and Combination Optimization and Its Application to Bronchoscope Guidance

Authors:
Kensaku Mori;Shunsuke Ota;Daisuke Deguchi;Takayuki Kitasaka;Yasuhito Suenaga;Shingo Iwano;Yosihnori Hasegawa;Hirotsugu Takabatake;Masaki Mori;Hiroshi Natori
Affiliations:
Graduate School of Information Science, Nagoya University, and Innovative Research Center for Preventive Medical Engineering, Nagoya University,;Graduate School of Information Science, Nagoya University,;Graduate School of Information Science, Nagoya University,;Innovative Research Center for Preventive Medical Engineering, Nagoya University, and Faculty of Management and Information Science, Aichi Institute of Technology,;Graduate School of Information Science, Nagoya University, and Innovative Research Center for Preventive Medical Engineering, Nagoya University,;Graduate School of Medicine, Nagoya University,;Innovative Research Center for Preventive Medical Engineering, Nagoya University, and Graduate School of Medicine, Nagoya University,;Sapporo Minami-Sanjyo Hospital,;Sapporo Kosei Hospital,;Nishioka Hospital,
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

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Abstract

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.