  Automation of brain tumor segmentation 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 especially critical in
medical diagnosis. This work studies 3D encoder-decoder architectures trained
with patch-based techniques to reduce memory consumption and decrease the
effect of unbalanced data. The different trained models are then used to create
an ensemble that leverages the properties of each model, thus increasing the
performance. We also introduce voxel-wise uncertainty information, both
epistemic and aleatoric using test-time dropout (TTD) and data-augmentation
(TTA) respectively. In addition, a hybrid approach is proposed that helps
increase the accuracy of the segmentation. The model and uncertainty estimation
measurements proposed in this work have been used in the BraTS'20 Challenge for
task 1 and 3 regarding tumor segmentation and uncertainty estimation.
