  In this work, we introduce the Global Planar Convolution module as a
building-block for fully-convolutional networks that aggregates global
information and, therefore, enhances the context perception capabilities of
segmentation networks in the context of brain tumor segmentation. We implement
two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet)
and present a novel architecture based on these two architectures, ContextNet,
that includes the proposed Global Planar Convolution module. We show that the
addition of such module eliminates the need of building networks with several
representation levels, which tend to be over-parametrized and to showcase slow
rates of convergence. Furthermore, we provide a visual demonstration of the
behavior of GPC modules via visualization of intermediate representations. We
finally participate in the 2018 edition of the BraTS challenge with our best
performing models, that are based on ContextNet, and report the evaluation
scores on the validation and the test sets of the challenge.
