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.
Original language | English |
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Pages | 125-129 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2017 |
Event | 2017 Eurographics Workshop on Visual Computing for Biology and Medicine - Bremen, Germany Duration: 07.09.2017 → 08.09.2017 Conference number: 160311 |
Conference
Conference | 2017 Eurographics Workshop on Visual Computing for Biology and Medicine |
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Abbreviated title | VCBM 2017 |
Country/Territory | Germany |
City | Bremen |
Period | 07.09.17 → 08.09.17 |
Research Areas and Centers
- Academic Focus: Biomedical Engineering