  Kidney tumor segmentation emerges as a new frontier of computer vision in
medical imaging. This is partly due to its challenging manual annotation and
great medical impact. Within the scope of the Kidney Tumor Segmentation
Challenge 2019, that is aiming at combined kidney and tumor segmentation, this
work proposes a novel combination of 3D U-Nets---collectively denoted
TuNet---utilizing the resulting kidney masks for the consecutive tumor
segmentation. The proposed method achieves a S{\o}rensen-Dice coefficient score
of 0.902 for the kidney, and 0.408 for the tumor segmentation, computed from a
five-fold cross-validation on the 210 patients available in the data.
