TY - CHAP
T1 - Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks
AU - Wolterink, Jelmer M.
AU - Leiner, Tim
AU - Viergever, Max A.
AU - Išgum, Ivana
PY - 2015
Y1 - 2015
N2 - The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.
AB - The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84947484146&origin=inward
U2 - https://doi.org/10.1007/978-3-319-24553-9_72
DO - https://doi.org/10.1007/978-3-319-24553-9_72
M3 - Chapter
VL - 9349
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 589
EP - 596
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
ER -