  Gliomas are among the most aggressive and deadly brain tumors. This paper
details the proposed Deep Neural Network architecture for brain tumor
segmentation from Magnetic Resonance Images. The architecture consists of a
cascade of three Deep Layer Aggregation neural networks, where each stage
elaborates the response using the feature maps and the probabilities of the
previous stage, and the MRI channels as inputs. The neuroimaging data are part
of the publicly available Brain Tumor Segmentation (BraTS) 2020 challenge
dataset, where we evaluated our proposal in the BraTS 2020 Validation and Test
sets. In the Test set, the experimental results achieved a Dice score of
0.8858, 0.8297 and 0.7900, with an Hausdorff Distance of 5.32 mm, 22.32 mm and
20.44 mm for the whole tumor, core tumor and enhanced tumor, respectively.
