  Brain tumor segmentation is a critical task for patient's disease management.
In order to automate and standardize this task, we trained multiple U-net like
neural networks, mainly with deep supervision and stochastic weight averaging,
on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 training
dataset. Two independent ensembles of models from two different training
pipelines were trained, and each produced a brain tumor segmentation map. These
two labelmaps per patient were then merged, taking into account the performance
of each ensemble for specific tumor subregions. Our performance on the online
validation dataset with test time augmentation were as follows: Dice of 0.81,
0.91 and 0.85; Hausdorff (95%) of 20.6, 4,3, 5.7 mm for the enhancing tumor,
whole tumor and tumor core, respectively. Similarly, our solution achieved a
Dice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mm
on the final test dataset, ranking us among the top ten teams. More complicated
training schemes and neural network architectures were investigated without
significant performance gain at the cost of greatly increased training time.
Overall, our approach yielded good and balanced performance for each tumor
subregion. Our solution is open sourced at
https://github.com/lescientifik/open_brats2020.
