Histopathologic patterns of nervous system tumors based on computer vision methods and whole slide imaging (WSI)

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).

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.

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.

Our method is a promising approach to automatic detection of nervous system tumors using virtual slides.