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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 likeneural networks, mainly with deep supervision and stochastic weight averaging,on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 trainingdataset. Two independent ensembles of models from two different trainingpipelines were trained, and each produced a brain tumor segmentation map. Thesetwo labelmaps per patient were then merged, taking into account the performanceof each ensemble for specific tumor subregions. Our performance on the onlinevalidation 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 aDice of 0.79, 0.89 and 0.84, as well as Hausdorff (95%) of 20.4, 6.7 and 19.5mmon the final test dataset, ranking us among the top ten teams. More complicatedtraining schemes and neural network architectures were investigated withoutsignificant performance gain at the cost of greatly increased training time.Overall, our approach yielded good and balanced performance for each tumorsubregion. Our solution is open sourced athttps://github.com/lescientifik/open_brats2020.

Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution