Predicting Stroke Lesion and Clinical Outcome with Random Forests

  • Oskar MaierEmail author
  • Heinz Handels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


The treatment of ischemic stroke requires fast decisions for which the potentially fatal risks of an intervention have to be weighted against the presumed benefits. Ideally, the treating physician could predict the outcome under different circumstances beforehand and thus make an informed treatment decision. To this end, this article presents two new methods: one for lesion outcome and one for clinical outcome prediction from multispectral magnetic resonance sequences. After extracting tailored image features, a random forest classifier respectively regressor is trained. Both approaches were submitted to the Ischemic Stroke Lesion Segmentation (ISLES) 2017 challenge and obtained a first and third place. The outcome underlines the robustness of our designed features and stresses the approach’s resilience against overfitting when faced with small training datasets.


Ischemic stroke Lesion segmentation Lesion outcome Clinical outcome mRS Magnetic resonance imaging Brain MR Random forest RDF ISLES 2016 


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversität zu LübeckLübeckGermany
  2. 2.Graduate School for Computing in Medicine and Life SciencesUniversität zu LübeckLübeckGermany

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