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

P. Löber, B. Stimpel, C. Syben, A. Maier, H. Ditt, P. Schramm, B. Raczkowski, A. Kemmling

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 languageEnglish
Pages125-129
Number of pages5
DOIs
Publication statusPublished - 2017
Event2017 Eurographics Workshop on Visual Computing for Biology and Medicine - Bremen, Germany
Duration: 07.09.201708.09.2017
Conference number: 160311

Conference

Conference2017 Eurographics Workshop on Visual Computing for Biology and Medicine
Abbreviated titleVCBM 2017
Country/TerritoryGermany
CityBremen
Period07.09.1708.09.17

Research Areas and Centers

  • Academic Focus: Biomedical Engineering

Fingerprint

Dive into the research topics of 'Automatic Thrombus Detection in Non-enhanced Computed Tomography Images in Patients With Acute Ischemic Stroke'. Together they form a unique fingerprint.

Cite this