  Accurate segmentation of different sub-regions of gliomas including
peritumoral edema, necrotic core, enhancing and non-enhancing tumor core from
multimodal MRI scans has important clinical relevance in diagnosis, prognosis
and treatment of brain tumors. However, due to the highly heterogeneous
appearance and shape, segmentation of the sub-regions is very challenging.
Recent development using deep learning models has proved its effectiveness in
the past several brain segmentation challenges as well as other semantic and
medical image segmentation problems. Most models in brain tumor segmentation
use a 2D/3D patch to predict the class label for the center voxel and variant
patch sizes and scales are used to improve the model performance. However, it
has low computation efficiency and also has limited receptive field. U-Net is a
widely used network structure for end-to-end segmentation and can be used on
the entire image or extracted patches to provide classification labels over the
entire input voxels so that it is more efficient and expect to yield better
performance with larger input size. Furthermore, instead of picking the best
network structure, an ensemble of multiple models, trained on different dataset
or different hyper-parameters, can generally improve the segmentation
performance. In this study we propose to use an ensemble of 3D U-Nets with
different hyper-parameters for brain tumor segmentation. Preliminary results
showed effectiveness of this model. In addition, we developed a linear model
for survival prediction using extracted imaging and non-imaging features,
which, despite the simplicity, can effectively reduce overfitting and
regression errors.
