Thrombus Detection in Non-contrast Head CT Using Graph Deep Learning

In case of an acute ischemic stroke, rapid diagnosis and removal of the occluding thrombus (blood clot) are crucial for a successful recovery. We present an automated thrombus detection system for non-contrast computed tomography (NCCT) images to improve the clinical workflow, where NCCT is typically acquired as a first-line imaging tool to identify the type of the stroke. The system consists of a candidate detection model and a subsequent classification model. The detection model generates a volumetric heatmap from the NCCT and extracts multiple potential clot candidates, sorted by their likeliness in descending order. The classification model performs reprioritization of these candidates using graph-based deep learning methods, where the candidates are no longer considered independently, but in a global context. It was optimized to classify the candidates as clot or no clot. The candidate detection model, which also serves as the main baseline, yields a ROC AUC of 79.8%, which is improved to 85.2% by the proposed graph-based classification model.