  Automatically segmenting sub-regions of gliomas (necrosis, edema and
enhancing tumor) and accurately predicting overall survival (OS) time from
multimodal MRI sequences have important clinical significance in diagnosis,
prognosis and treatment of gliomas. However, due to the high degree variations
of heterogeneous appearance and individual physical state, the segmentation of
sub-regions and OS prediction are very challenging. To deal with these
challenges, we utilize a 3D dilated multi-fiber network (DMFNet) with weighted
dice loss for brain tumor segmentation, which incorporates prior volume
statistic knowledge and obtains a balance between small and large objects in
MRI scans. For OS prediction, we propose a DenseNet based 3D neural network
with position encoding convolutional layer (PECL) to extract meaningful
features from T1 contrast MRI, T2 MRI and previously segmented subregions. Both
labeled data and unlabeled data are utilized to prevent over-fitting for
semi-supervised learning. Those learned deep features along with handcrafted
features (such as ages, volume of tumor) and position encoding segmentation
features are fed to a Gradient Boosting Decision Tree (GBDT) to predict a
specific OS day
