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

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dc.contributor.authorLöber, Patricken_US
dc.contributor.authorStimpel, Bernharden_US
dc.contributor.authorSyben, Christopheren_US
dc.contributor.authorMaier, Andreasen_US
dc.contributor.authorDitt, Hendriken_US
dc.contributor.authorSchramm, Peteren_US
dc.contributor.authorRaczkowski, Boyen_US
dc.contributor.authorKemmling, Andréen_US
dc.contributor.editorStefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Riederen_US
dc.date.accessioned2017-09-06T07:12:41Z
dc.date.available2017-09-06T07:12:41Z
dc.date.issued2017
dc.description.abstractIn 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.en_US
dc.description.sectionheadersShort Papers
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20171245
dc.identifier.isbn978-3-03868-036-9
dc.identifier.issn2070-5786
dc.identifier.pages125-129
dc.identifier.urihttps://doi.org/10.2312/vcbm.20171245
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vcbm20171245
dc.publisherThe Eurographics Associationen_US
dc.subjectCCS Concepts
dc.subjectComputing methodologies
dc.subjectClassification and regression trees
dc.subjectApplied computing
dc.subjectHealth care information systems
dc.titleAutomatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Strokeen_US
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VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine