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Various types of atherosclerotic plaque and varying grades of stenosis couldlead to different management of patients with coronary artery disease.Therefore, it is crucial to detect and classify the type of coronary arteryplaque, as well as to detect and determine the degree of coronary arterystenosis. This study includes retrospectively collected clinically obtainedcoronary CT angiography (CCTA) scans of 163 patients. To perform automaticanalysis for coronary artery plaque and stenosis classification, a multi-taskrecurrent convolutional neural network is applied on multi-planar reformatted(MPR) images of the coronary arteries. First, a 3D convolutional neural networkis utilized to extract features along the coronary artery. Subsequently, theextracted features are aggregated by a recurrent neural network that performstwo simultaneous multi-class classification tasks. In the first task, thenetwork detects and characterizes the type of the coronary artery plaque (noplaque, non-calcified, mixed, calcified). In the second task, the networkdetects and determines the anatomical significance of the coronary arterystenosis (no stenosis, non-significant i.e. <50% luminal narrowing, significanti.e. >50% luminal narrowing). For detection and classification of coronaryplaque, the method achieved an accuracy of 0.77. For detection andclassification of stenosis, the method achieved an accuracy of 0.80. Theresults demonstrate that automatic detection and classification of coronaryartery plaque and stenosis are feasible. This may enable automated triage ofpatients to those without coronary plaque and those with coronary plaque andstenosis in need for further cardiovascular workup.

A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography