title: Histopathologic patterns of nervous system tumors based on computer vision methods and whole slide imaging (WSI).
abstract: BACKGROUND
  Making an automatic diagnosis based on virtual slides and whole slide imaging or even determining whether a case belongs to a single class, representing a specific disease, is a big challenge. In this work we focus on WHO Classification of Tumours of the Central Nervous System. We try to design a method which allows to automatically distinguish virtual slides which contain histopathologic patterns characteristic of glioblastoma--pseudopalisading necrosis and discriminate cases with neurinoma (schwannoma), which contain similar structures--palisading (Verocay bodies).
  
  
  METHODS
  Our method is based on computer vision approaches like structural analysis and shape descriptors. We start with image segmentation in a virtual slide, find specific patterns and use a set of features which can describe pseudopalisading necrosis and distinguish it from palisades. Type of structures found in a slide decides about its classification.
  
  
  RESULTS
  Described method is tested on a set of 49 virtual slides, captured using robotic microscope. Results show that 82% of glioblastoma cases and 90% of neurinoma cases were correctly identified by the proposed algorithm.
  
  
  CONCLUSIONS
  Our method is a promising approach to automatic detection of nervous system tumors using virtual slides.
authors: Poznan University of Technology, Poznan, Poland. slawowski@gmail.com
authors: Slawomir
authors: S
authors: Walkowski
authors: Janusz
authors: J
authors: Szymas
mesh_terms: D000465:Algorithms; D016543:Central Nervous System Neoplasms / Q000145:classification* / Q000175:diagnosis*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*