Löber, PatrickStimpel, BernhardSyben, ChristopherMaier, AndreasDitt, HendrikSchramm, PeterRaczkowski, BoyKemmling, AndréStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder2017-09-062017-09-062017978-3-03868-036-92070-5786https://doi.org/10.2312/vcbm.20171245https://diglib.eg.org:443/handle/10.2312/vcbm20171245In 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 ConceptsComputing methodologiesClassification and regression treesApplied computingHealth care information systems10.2312/vcbm.20171245125-129