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In this work, we introduce the Global Planar Convolution module as abuilding-block for fully-convolutional networks that aggregates globalinformation and, therefore, enhances the context perception capabilities ofsegmentation networks in the context of brain tumor segmentation. We implementtwo 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 theaddition of such module eliminates the need of building networks with severalrepresentation levels, which tend to be over-parametrized and to showcase slowrates of convergence. Furthermore, we provide a visual demonstration of thebehavior of GPC modules via visualization of intermediate representations. Wefinally participate in the 2018 edition of the BraTS challenge with our bestperforming models, that are based on ContextNet, and report the evaluationscores on the validation and the test sets of the challenge.

Global Planar Convolutions for improved context aggregation in Brain Tumor Segmentation