Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning

Abstract

Deep Learning is expanding in the detection and diagnosis of abnormalities , including coronary artery calcification (CAC), in CT. CACs can also be visualized on low-dose thoracic screening CTs (LDCT), and thus, in this study, deep learning is investigated for the detection of CACs and assessment of their severity on LDCT images. The study dataset included 863 LDCT cases, each assigned a case severity score, which is related to the Agatston score, ranging between 0 and 12 (0 = no CAC present, 12 = severe CACs). Within the cardiac region, 224 × 224 pixel ROIs were extracted from each CT slice and input to a convolutional neural network (CNN). CNN-based features were extracted using a pre-trained VGG19 and merged with a support vector machine (SVM) yielding a slice likelihood score of the presence of CACs . Case prediction scores were obtained by using the maximum and mean scores of all slices belonging to that case. Area under the ROC curve (AUC) was used as a metric to assess the discrimination performance level. Using a randomly selected subset of images containing similar amounts of each severity subtype, the SVM performed better using the max slice score per case (AUC = 0.79, standard error = 0.03). While this AUC value does not reach those found in similar studies for diagnostic CT and cardiac CT angiography, this study demonstrates potential for deep learning use in LDCT screening programs.