BibTex format
@article{Zhang:2021:10.1016/j.ejrad.2020.109420,
author = {Zhang, N and Yang, G and Zhang, W and Wang, W and Zhou, Z and Zhang, H and Xu, L and Chen, Y},
doi = {10.1016/j.ejrad.2020.109420},
journal = {European Journal of Radiology},
title = {Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications},
url = {http://dx.doi.org/10.1016/j.ejrad.2020.109420},
volume = {134},
year = {2021}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - ObjectivesTo develop a fully automatic multiview shape constraint framework for comprehensive coronary artery calcium scores (CACS) quantification via deep learning on nonenhanced cardiac CT images.MethodsIn this retrospective single-centre study, a multi-task deep learning framework was proposed to detect and quantify coronary artery calcification from CT images collected between October 2018 and March 2019. A total of 232 non-contrast cardiac-gated CT scans were retrieved and studied (80 % for model training and 20 % for testing). CACS results of testing datasets (n = 46), including Agatston score, calcium volume score, calcium mass score, were calculated fully automatically and manually at total and vessel-specific levels, respectively.ResultsNo significant differences were found in CACS quantification obtained using automatic or manual methods at total and vessel-specific levels (Agatston score: automatic 535.3 vs. manual 542.0, P = 0.993; calcium volume score: automatic 454.2 vs. manual 460.6, P = 0.990; calcium mass score: automatic 128.9 vs. manual 129.5, P = 0.992). Compared to the ground truth, the number of calcified vessels can be accurate recognized automatically (total: automatic 107 vs. manual 102, P = 0.125; left main artery: automatic 15 vs. manual 14, P = 1.000 ; left ascending artery: automatic 37 vs. manual 37, P = 1.000; left circumflex artery: automatic 22 vs. manual 20, P = 0.625; right coronary artery: automatic 33 vs. manual 31, P = 0.500). At the patient’s level, there was no statistic difference existed in the classification of Agatston scoring (P = 0.317) and the number of calcified vessels (P = 0.102) between the automatic and manual results.ConclusionsThe proposed framework can achieve reliable and comprehensive quantification for the CACS, including the calcified extent and distr
AU - Zhang,N
AU - Yang,G
AU - Zhang,W
AU - Wang,W
AU - Zhou,Z
AU - Zhang,H
AU - Xu,L
AU - Chen,Y
DO - 10.1016/j.ejrad.2020.109420
PY - 2021///
SN - 0720-048X
TI - Fully automatic framework for comprehensive coronary artery calcium scores analysis on non-contrast cardiac-gated CT scan: Total and vessel-specific quantifications
T2 - European Journal of Radiology
UR - http://dx.doi.org/10.1016/j.ejrad.2020.109420
UR - http://hdl.handle.net/10044/1/85726
VL - 134
ER -
