Image Features for Brain Lesion Segmentation Using Random Forests

  • Oskar MaierEmail author
  • Matthias Wilms
  • Heinz Handels
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)


From clinical practice as well as research methods arises the need for accurate, reproducible and reliable segmentation of pathological areas from brain MR scans. This paper describes a set of hand-selected, voxel-based image features highly suitable for the tissue discrimination task. Embedded in a random decision forest framework, the proposed method was applied to sub-acute ischemic stroke (ISLES 2015 - SISS), acute ischemic stroke (ISLES 2015 - SPES) and glioma (BRATS 2015) segmentation with only minor adaptation. For all of these three challenges, our generic approach received high ranks, among them a second place. The outcome underlines the robustness of our features for segmentation in brain MR, while simultaneously stressing the necessity for highly specialized solution to achieve state-of-the-art performance.


Ischemic stroke Lesion segmentation Magnetic resonance imaging Brain MR MRI Random forest RDF Acute Sub-acute Glioma Tumor ISLES 2015 BRATS 2015 SISS SPES 


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Copyright information

© Springer International Publishing Switzerland 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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