Poster (download) Sergey Golushko1, Mikhail Amelin2, Bair Tuchinov3, Evgeniya Amelina4, Nikolay Tolstokulakov5, Evgeniy Pavlovskiy6, Vladimir Groza71Novosibirsk State University, s.k.golushko@gmail.com2FSBI \”Federal Neurosurgical Center\”, amelin81@gmail.com3Novosibirsk State University, bairts@gmail.com4Novosibirsk State University, amelina.evgenia@gmail.com5Novosibirsk State University, n.tolstokulakov@g.nsu.ru6Novosibirsk State University, pavlovskiy@post.nsu.ru7Median Technologies, vladimir.groza@gmail.com Automatic brain tumor segmentation from CT or MRI scans is one of the crucial problems among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying patients with various pathologies.In this paper, we report the results of the research project \”Brain Tumor Segmentation\” organized in conjunction with the Federal Neurosurgical Center. Several state-of-the-art tumor segmentation algorithms were applied to a set of 100 MRI scans of meningioma, neurinoma and glioma patients – manually annotated by up to three raters – and to 100 comparable scans obtained using the automated tumor multi-region segmentation. Quantitative evaluations revealed a considerable agreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 85-90\\%). We found that different algorithms worked best for different sub-regions, but no single algorithm ranked in the top for all subregions simultaneously.