  Brain tumor segmentation from multiple Magnetic Resonance Imaging (MRI)
modalities is a challenging task in medical image computation. The main
challenges lie in the generalizability to a variety of scanners and imaging
protocols. In this paper, we explore strategies to increase model robustness
without increasing inference time. Towards this aim, we explore finding a
robust ensemble from models trained using different losses, optimizers, and
train-validation data split. Importantly, we explore the inclusion of a
transformer in the bottleneck of the U-Net architecture. While we find
transformer in the bottleneck performs slightly worse than the baseline U-Net
in average, the generalized Wasserstein Dice loss consistently produces
superior results. Further, we adopt an efficient test time augmentation
strategy for faster and robust inference. Our final ensemble of seven 3D U-Nets
with test-time augmentation produces an average dice score of 89.4% and an
average Hausdorff 95% distance of 10.0 mm when evaluated on the BraTS 2021
testing dataset. Our code and trained models are publicly available at
https://github.com/LucasFidon/TRABIT_BraTS2021.
