TY - JOUR
T1 - Automatic coronary calcium scoring in low-dose chest computed tomography
AU - Išgum, Ivana
AU - Prokop, Mathias
AU - Niemeijer, Meindert
AU - Viergever, Max A.
AU - van Ginneken, Bram
PY - 2012
Y1 - 2012
N2 - The calcium burden as estimated fromnon-ECG-synchronized computed tomography (CT) exams acquired in screening of heavy smokers has been shown to be a strong predictor of cardiovascular events. We present a method for automatic coronary calcium scoring with low-dose, non-contrast-enhanced, non-ECG-synchronized chest CT. First, a probabilistic coronary calcium map was created using multi-atlas segmentation. This map assigned an a priori probability for the presence of coronary calcifications at every location in a scan. Subsequently, a statistical pattern recognition system was designed to identify coronary calcifications by texture, size, and spatial features; the spatial featureswere computed using the coronary calcium map. The detected calcifications were quantified in terms of volume and Agatston score. The best results were obtained by merging the results of three different supervised classification systems, namely direct classification with a nearest neighbor classifier, and two-stage classification with nearest neighbor and support vector machine classifiers.We used a total of 231 test scans containing 45 674mm3 of coronary calcifications. The presented method detected on average 157/198mm3 (sensitivity 79.2%) of coronary calcium volume with on average 4 mm3 false positive volume. Calcium scoring can be performed automatically in low-dose, noncontrast enhanced, non-ECG-synchronized chest CT in screening of heavy smokers to identify subjects who might benefit from preventive treatment. © 2012 IEEE.
AB - The calcium burden as estimated fromnon-ECG-synchronized computed tomography (CT) exams acquired in screening of heavy smokers has been shown to be a strong predictor of cardiovascular events. We present a method for automatic coronary calcium scoring with low-dose, non-contrast-enhanced, non-ECG-synchronized chest CT. First, a probabilistic coronary calcium map was created using multi-atlas segmentation. This map assigned an a priori probability for the presence of coronary calcifications at every location in a scan. Subsequently, a statistical pattern recognition system was designed to identify coronary calcifications by texture, size, and spatial features; the spatial featureswere computed using the coronary calcium map. The detected calcifications were quantified in terms of volume and Agatston score. The best results were obtained by merging the results of three different supervised classification systems, namely direct classification with a nearest neighbor classifier, and two-stage classification with nearest neighbor and support vector machine classifiers.We used a total of 231 test scans containing 45 674mm3 of coronary calcifications. The presented method detected on average 157/198mm3 (sensitivity 79.2%) of coronary calcium volume with on average 4 mm3 false positive volume. Calcium scoring can be performed automatically in low-dose, noncontrast enhanced, non-ECG-synchronized chest CT in screening of heavy smokers to identify subjects who might benefit from preventive treatment. © 2012 IEEE.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84870488848&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/22961297
U2 - https://doi.org/10.1109/TMI.2012.2216889
DO - https://doi.org/10.1109/TMI.2012.2216889
M3 - Article
C2 - 22961297
SN - 0278-0062
VL - 31
SP - 2322
EP - 2334
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 12
ER -