TY - GEN
T1 - Model-guided directional minimal path for fully automatic extraction of coronary centerlines from cardiac CTA
AU - Liu, Liu
AU - Shi, Wenzhe
AU - Rueckert, Daniel
AU - Hu, Mingxing
AU - Ourselin, Sebastien
AU - Zhuang, Xiahai
PY - 2013
Y1 - 2013
N2 - Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac CTA. In this paper, we propose a model-guided approach, the directional minimal path, for the centerline extraction. The proposed method is based on the minimal path algorithm and a prior coronary model is used. The model is first registered to the unseen image. Then, the start point and end point for the minimal path algorithm are provided by the model to automate the centerline extraction process. Also, the direction information of the coronary model is used to guide the path tracking of the minimal path procedure. This directional tracking improves the robustness and accuracy of the centerline extraction. Finally, the proposed method can automatically recognize the branches of the extracted coronary artery using the prior information in the model. We validated the proposed method by extracting the three main coronary branches. The mean accuracy of the 56 cases was 1.32±0.81 mm and the detection ratio was 88.7%.
AB - Extracting centerlines of coronary arteries is a challenging but important task in clinical applications of cardiac CTA. In this paper, we propose a model-guided approach, the directional minimal path, for the centerline extraction. The proposed method is based on the minimal path algorithm and a prior coronary model is used. The model is first registered to the unseen image. Then, the start point and end point for the minimal path algorithm are provided by the model to automate the centerline extraction process. Also, the direction information of the coronary model is used to guide the path tracking of the minimal path procedure. This directional tracking improves the robustness and accuracy of the centerline extraction. Finally, the proposed method can automatically recognize the branches of the extracted coronary artery using the prior information in the model. We validated the proposed method by extracting the three main coronary branches. The mean accuracy of the 56 cases was 1.32±0.81 mm and the detection ratio was 88.7%.
UR - http://www.scopus.com/inward/record.url?scp=84885716250&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40811-3_68
DO - 10.1007/978-3-642-40811-3_68
M3 - Conference contribution
C2 - 24505709
AN - SCOPUS:84885716250
SN - 9783642408106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 542
EP - 549
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -