TY - GEN
T1 - Augmented display of anatomical names of bronchial branches for bronchoscopy assistance
AU - Ota, Shunsuke
AU - Deguchi, Daisuke
AU - Kitasaka, Takayuki
AU - Mori, Kensaku
AU - Suenaga, Yasuhito
AU - Hasegawa, Yoshinori
AU - Imaizumi, Kazuyoshi
AU - Takabatake, Hirotsugu
AU - Mori, Masaki
AU - Natori, Hiroshi
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/50249107920
UR - https://www.scopus.com/pages/publications/50249107920#tab=citedBy
U2 - 10.1007/978-3-540-79982-5_41
DO - 10.1007/978-3-540-79982-5_41
M3 - Conference contribution
AN - SCOPUS:50249107920
SN - 3540799818
SN - 9783540799818
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 377
EP - 384
BT - Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings
T2 - 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008
Y2 - 1 August 2008 through 2 August 2008
ER -