The Siberian multimodal brain tumor image segmentation dataset (SBT)

Poster (download)

514

Sergey Golushko¹, Mikhail Amelin², Bair Tuchinov³, Evgeniya Amelina⁴, Nikolay Tolstokulakov⁵, Evgeniy Pavlovskiy⁶, Vladimir Groza^7
1Novosibirsk State University, s.k.golushko@gmail.com
²FSBI \”Federal Neurosurgical Center\”, amelin81@gmail.com
³Novosibirsk State University, bairts@gmail.com
⁴Novosibirsk State University, amelina.evgenia@gmail.com
⁵Novosibirsk State University, n.tolstokulakov@g.nsu.ru
⁶Novosibirsk State University, pavlovskiy@post.nsu.ru
⁷Median 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.