TY - GEN
T1 - Automatic mediastinal lymph node detection in chest CT
AU - Feuerstein, Marco
AU - Deguchi, Daisuke
AU - Kitasaka, Takayuki
AU - Iwano, Shingo
AU - Imaizumi, Kazuyoshi
AU - Hasegawa, Yoshinori
AU - Suenaga, Yasuhito
AU - Mori, Kensaka
PY - 2009
Y1 - 2009
N2 - Computed tomography (CT) of the chest is a very common staging investigation for the assessment of mediastinal, hilar, and intrapulmonary lymph nodes in the context of lung cancer. In the current clinical workflow, the detection and assessment of lymph nodes is usually performed manually, which can be error-prone and timeconsuming. We therefore propose a method for the automatic detection of mediastinal, hilar, and intrapulmonary lymph node candidates in contrast-enhanced chest CT. Based on the segmentation of important mediastinal anatomy (bronchial tree, aortic arch) and making use of anatomical knowledge, we utilize Hessian eigenvalues to detect lymph node candidates. As lymph nodes can be characterized as blob-like structures of varying size and shape within a specific intensity interval, we can utilize these characteristics to reduce the number of false positive candidates significantly. We applied our method to 5 cases suspected to have lung cancer. The processing time of our algorithm did not exceed 6 minutes, and we achieved an average sensitivity of 82.1% and an average precision of 13.3%.
AB - Computed tomography (CT) of the chest is a very common staging investigation for the assessment of mediastinal, hilar, and intrapulmonary lymph nodes in the context of lung cancer. In the current clinical workflow, the detection and assessment of lymph nodes is usually performed manually, which can be error-prone and timeconsuming. We therefore propose a method for the automatic detection of mediastinal, hilar, and intrapulmonary lymph node candidates in contrast-enhanced chest CT. Based on the segmentation of important mediastinal anatomy (bronchial tree, aortic arch) and making use of anatomical knowledge, we utilize Hessian eigenvalues to detect lymph node candidates. As lymph nodes can be characterized as blob-like structures of varying size and shape within a specific intensity interval, we can utilize these characteristics to reduce the number of false positive candidates significantly. We applied our method to 5 cases suspected to have lung cancer. The processing time of our algorithm did not exceed 6 minutes, and we achieved an average sensitivity of 82.1% and an average precision of 13.3%.
UR - http://www.scopus.com/inward/record.url?scp=66749157188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=66749157188&partnerID=8YFLogxK
U2 - 10.1117/12.811101
DO - 10.1117/12.811101
M3 - Conference contribution
AN - SCOPUS:66749157188
SN - 9780819475114
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2009
T2 - Medical Imaging 2009: Computer-Aided Diagnosis
Y2 - 10 February 2009 through 12 February 2009
ER -