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

Abstract
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.
Description

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@inproceedings{
10.2312:vcbm.20171245
, booktitle = {
Eurographics Workshop on Visual Computing for Biology and Medicine
}, editor = {
Stefan Bruckner and Anja Hennemuth and Bernhard Kainz and Ingrid Hotz and Dorit Merhof and Christian Rieder
}, title = {{
Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke
}}, author = {
Löber, Patrick
and
Stimpel, Bernhard
and
Syben, Christopher
and
Maier, Andreas
and
Ditt, Hendrik
and
Schramm, Peter
and
Raczkowski, Boy
and
Kemmling, André
}, year = {
2017
}, publisher = {
The Eurographics Association
}, ISSN = {
2070-5786
}, ISBN = {
978-3-03868-036-9
}, DOI = {
10.2312/vcbm.20171245
} }
Citation