Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
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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}
}