  Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to
assess the diagnostic and treatment of the disease. In recent years,
convolutional neural networks (CNNs) have shown improved results in the task.
However, high memory consumption is still a problem in 3D-CNNs. Moreover, most
methods do not include uncertainty information, which is specially critical in
medical diagnosis. This work proposes a 3D encoder-decoder architecture, based
on V-Net \cite{vnet} which is trained with patching techniques to reduce memory
consumption and decrease the effect of unbalanced data. We also introduce
voxel-wise uncertainty, both epistemic and aleatoric using test-time dropout
and data-augmentation respectively. Uncertainty maps can provide extra
information to expert neurologists, useful for detecting when the model is not
confident on the provided segmentation.
