Share Email Print
cover

Proceedings Paper

Automated anatomical labeling of bronchial branches using multiple classifiers and its application to bronchoscopy guidance based on fusion of virtual and real bronchoscopy
Author(s): Shunsuke Ota; Daisuke Deguchi; Takayuki Kitasaka; Kensaku Mori; Yasuhito Suenaga; Yoshinori Hasegawa; Kazuyoshi Imaizumi; Hirotsugu Takabatake; Masaki Mori; Hiroshi Natori
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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 views.

Paper Details

Date Published: 3 April 2008
PDF: 12 pages
Proc. SPIE 6916, Medical Imaging 2008: Physiology, Function, and Structure from Medical Images, 69160G (3 April 2008); doi: 10.1117/12.771834
Show Author Affiliations
Shunsuke Ota, Nagoya Univ. (Japan)
Daisuke Deguchi, Nagoya Univ. (Japan)
Takayuki Kitasaka, Nagoya Univ. (Japan)
Kensaku Mori, Nagoya Univ. (Japan)
Yasuhito Suenaga, Nagoya Univ. (Japan)
Yoshinori Hasegawa, Nagoya Univ. School of Medicine (Japan)
Kazuyoshi Imaizumi, Nagoya Univ. School of Medicine (Japan)
Hirotsugu Takabatake, Sapporo Minami Sanjo Hospital (Japan)
Masaki Mori, Sapporo-Kosei General Hospital (Japan)
Hiroshi Natori, Keiwakai Nishioka Hospital (Japan)


Published in SPIE Proceedings Vol. 6916:
Medical Imaging 2008: Physiology, Function, and Structure from Medical Images
Xiaoping P. Hu; Anne V. Clough, Editor(s)

© SPIE. Terms of Use
Back to Top