Augmented Display of Anatomical Names of Bronchial Branches for Bronchoscopy Assistance

Authors:
Shunsuke Ota;Daisuke Deguchi;Takayuki Kitasaka;Kensaku Mori;Yasuhito Suenaga;Yoshinori Hasegawa;Kazuyoshi Imaizumi;Hirotsugu Takabatake;Masaki Mori;Hiroshi Natori
Affiliations:
Graduate School of Information Science, Nagoya University, Japan;Mext Innovative Research Center for Preventive Medical Engineering, Nagoya University, Japan;Mext Innovative Research Center for Preventive Medical Engineering, Nagoya University, Japan and Faculty of Management and Information Science, Aichi Institute of Technology, Japan;Graduate School of Information Science, Nagoya University, Japan and Mext Innovative Research Center for Preventive Medical Engineering, Nagoya University, Japan;Graduate School of Information Science, Nagoya University, Japan and Mext Innovative Research Center for Preventive Medical Engineering, Nagoya University, Japan;School of Medicine, Nagoya University, Japan;School of Medicine, Nagoya University, Japan;Sapporo Minami Sanjo Hospital, Japan;Sapporo-Kosei General Hospital, Japan;Keiwakai Nishioka Hospital, Japan
Venue:
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Year:
2008

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Abstract

This paper presents a method for an automated anatomical labeling of bronchial branches (ALBB) for augmented display of its result for bronchoscopy assistance. A method for automated ALBB plays an important role for realizing an augmented display of anatomical names of bronchial branches. The ALBB problem can be considered as a problem that each bronchial branch is classified into the bronchial name to which it belongs. Therefore, the proposed method constructs classifiers that output anatomical names of bronchial branches by employing the machine-learning approach. The proposed method consists of four steps: (a) extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class AdaBoost technique, (c) automated classification of bronchial branches by using the constructed classifiers, and (d) an augmented display of anatomical names of bronchial branches. We applied the proposed method to 71 cases of 3D CT datasets. We evaluated the ALBB results by leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical names to bronchial branches of 90.1% up to segmental lobe branches. Also, we confirmed that an augmented display of the ALBB results was quite useful to assist bronchoscopy.