TY - GEN
T1 - Machine learning-based automatic detection of pulmonary trunk
AU - Wu, Hong
AU - Deng, Kun
AU - Liang, Jianming
PY - 2011
Y1 - 2011
N2 - Pulmonary embolism is a common cardiovascular emergency with about 600,000 cases occurring annually and causing approximately 200,000 deaths in the US. CT pulmonary angiography (CTPA) has become the reference standard for PE diagnosis, but the interpretation of these large image datasets is made complex and time consuming by the intricate branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus of contrast and inhomogeneities with the pulmonary arterial blood pool. To meet this challenge, several approaches for computer aided diagnosis of PE in CTPA have been proposed. However, none of these approaches is capable of detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans. To overcome these shortcomings, it requires highly efficient and accurate identification of the pulmonary trunk. For this very purpose, in this paper, we present a machine learning based approach for automatically detecting the pulmonary trunk. Our idea is to train a cascaded AdaBoost classifier with a large number of Haar features extracted from CTPA image samples, so that the pulmonary trunk can be automatically identified by sequentially scanning the CTPA images and classifying each encountered sub-image with the trained classifier. Our approach outperforms an existing anatomy-based approach, requiring no explicit representation of anatomical knowledge and achieving a nearly 100% accuracy tested on a large number of cases.
AB - Pulmonary embolism is a common cardiovascular emergency with about 600,000 cases occurring annually and causing approximately 200,000 deaths in the US. CT pulmonary angiography (CTPA) has become the reference standard for PE diagnosis, but the interpretation of these large image datasets is made complex and time consuming by the intricate branching structure of the pulmonary vessels, a myriad of artifacts that may obscure or mimic PEs, and suboptimal bolus of contrast and inhomogeneities with the pulmonary arterial blood pool. To meet this challenge, several approaches for computer aided diagnosis of PE in CTPA have been proposed. However, none of these approaches is capable of detecting central PEs, distinguishing the pulmonary artery from the vein to effectively remove any false positives from the veins, and dynamically adapting to suboptimal contrast conditions associated the CTPA scans. To overcome these shortcomings, it requires highly efficient and accurate identification of the pulmonary trunk. For this very purpose, in this paper, we present a machine learning based approach for automatically detecting the pulmonary trunk. Our idea is to train a cascaded AdaBoost classifier with a large number of Haar features extracted from CTPA image samples, so that the pulmonary trunk can be automatically identified by sequentially scanning the CTPA images and classifying each encountered sub-image with the trained classifier. Our approach outperforms an existing anatomy-based approach, requiring no explicit representation of anatomical knowledge and achieving a nearly 100% accuracy tested on a large number of cases.
KW - AdaBoost
KW - Automatic machine learning based detection
KW - Cascade
KW - Haar
KW - Pulmonary Embolism
KW - Pulmonary Trunk
UR - http://www.scopus.com/inward/record.url?scp=79955750552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79955750552&partnerID=8YFLogxK
U2 - 10.1117/12.878397
DO - 10.1117/12.878397
M3 - Conference contribution
AN - SCOPUS:79955750552
SN - 9780819485052
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2011
T2 - Medical Imaging 2011: Computer-Aided Diagnosis
Y2 - 15 February 2011 through 17 February 2011
ER -