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.
|Title of host publication||Bildverarbeitung für die Medizin 2019|
|Number of pages||6|
|Publication status||Published - 01.01.2019|
|Event||Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Germany|
Duration: 17.03.2019 → 19.03.2019
Conference number: 224899