Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT

Christian Lucas, Jonas J. Schöttler, André Kemmling, Linda F. Aulmann, Mattias P. Heinrich


Intervention time plays a very important role for stroke outcome and affects different therapy paths. Automatic detection of an ischemic condition during emergency imaging could draw the attention of a radiologist directly to the thrombotic clot. Considering an appropriate early treatment, the immediate automatic detection of a clot could lead to a better patient outcome by reducing time-to-treatment. We present a two-stage neural network to automatically segment and classify clots in the MCA+ICA region for a fast pre-selection of positive cases to support patient triage and treatment planning. Our automatic method achieves an area under the receiver operating curve (AUROC) of 0.99 for the correct positive/negative classification on unseen test data.

Original languageEnglish
Title of host publicationBildverarbeitung für die Medizin 2019
Number of pages6
PublisherSpringer Verlag
Publication date01.01.2019
ISBN (Print)978-3-658-25325-7
ISBN (Electronic)978-3-658-25326-4
Publication statusPublished - 01.01.2019
EventWorkshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Germany
Duration: 17.03.201919.03.2019
Conference number: 224899


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