This paper presents a method for automated anatomical labeling of bronchial branches (ALBB) extracted from
3D CT datasets. The proposed method constructs classifiers that output anatomical names of bronchial branches
by employing the machine-learning approach. We also present its application to a bronchoscopy guidance system.
Since the bronchus has a complex tree structure, bronchoscopists easily tend to get disoriented and lose the way
to a target location. A bronchoscopy guidance system is strongly expected to be developed to assist bronchoscopists.
In such guidance system, automated presentation of anatomical names is quite useful information for
bronchoscopy. Although several methods for automated ALBB were reported, most of them constructed models
taking only variations of branching patterns into account and did not consider those of running directions.
Since the running directions of bronchial branches differ greatly in individuals, they could not perform ALBB
accurately when running directions of bronchial branches were different from those of models. Our method tries
to solve such problems by utilizing the machine-learning approach. Actual procedure consists of three steps: (a)
extraction of bronchial tree structures from 3D CT datasets, (b) construction of classifiers using the multi-class
AdaBoost technique, and (c) automated classification of bronchial branches by using the constructed classifiers.
We applied the proposed method to 51 cases of 3D CT datasets. The constructed classifiers were evaluated by
leave-one-out scheme. The experimental results showed that the proposed method could assign correct anatomical
names to bronchial branches of 89.1% up to segmental lobe branches. Also, we confirmed that it was quite
useful to assist the bronchoscopy by presenting anatomical names of bronchial branches on real bronchoscopic