  In the WHO glioma classification guidelines grade, IDH mutation and 1p19q
co-deletion play a central role as they are important markers for prognosis and
optimal therapy planning. Therefore, we propose a fully automatic, MRI based,
3D pipeline for glioma segmentation and classification. The designed
segmentation network was a 3D U-Net achieving an average whole tumor dice score
of 90%. After segmentation, the 3D tumor ROI is extracted and fed into the
multi-task classification network. The network was trained and evaluated on a
large heterogeneous dataset of 628 patients, collected from The Cancer Imaging
Archive and BraTS 2019 databases. Additionally, the network was validated on an
independent dataset of 110 patients retrospectively acquired at the Ghent
University Hospital (GUH). Classification AUC scores are 0.93, 0.94 and 0.82 on
the TCIA test data and 0.94, 0.86 and 0.87 on the GUH data for grade, IDH and
1p19q status respectively.
