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

  • Christian LucasEmail author
  • Jonas J. Schöttler
  • André Kemmling
  • Linda F. Aulmann
  • Mattias P. Heinrich
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


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.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Christian Lucas
    • 1
    Email author
  • Jonas J. Schöttler
    • 1
  • André Kemmling
    • 2
  • Linda F. Aulmann
    • 3
  • Mattias P. Heinrich
    • 1
  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckDeutschland
  2. 2.Department of Clinical RadiologyUniversity Hospital MünsterMünsterDeutschland
  3. 3.Institute of NeuroradiologyUniversity Medical Center Schleswig-HolsteinLübeckDeutschland

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