  The U-Net is arguably the most successful segmentation architecture in the
medical domain. Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor
Segmentation Challenge and attempt to improve upon it by augmenting it with
residual and pre-activation residual blocks. Cross-validation results on the
training cases suggest only very minor, barely measurable improvements. Due to
marginally higher dice scores, the residual 3D U-Net is chosen for test set
prediction. With a Composite Dice score of 91.23 on the test set, our method
outperformed all 105 competing teams and won the KiTS2019 challenge by a small
margin.
