Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke

In case of an ischemic stroke, identifying and removing blood clots is crucial for a successful recovery. We present a novel method to automatically detect vascular occlusion in non-enhanced computed tomography (NECT) images. Possible hyperdense thrombus candidates are extracted by thresholding and connected component clustering. A set of different features is computed to describe the objects, and a Random Forest classifier is applied to predict them. Thrombus classification yields 98.7% sensitivity with 6.7 false positives per volume, and 91.1% sensitivity with 2.7 false positives per volume. The classifier assigns a clot probability ≥ 90% for every thrombus with a volume larger than 100 mm3 or with a length above 23 mm, and can be used as a reliable method to detect blood clots. CCS Concepts •Computing methodologies → Classification and regression trees; •Applied computing → Health care information systems;