  Purpose: The segmentation of brain tumors is one of the most active areas of
medical image analysis. While current methods perform superhuman on benchmark
data sets, their applicability in daily clinical practice has not been
evaluated. In our work we investigate the generalization behavior of deep
neural networks in this scenario.
  Approach: We evaluate the performance of three state-of-the-art methods, a
basic U-net architecture and a cascadic Mumford-Shah approach. We also propose
two simple modifications (which do not change the topology) to improve
generalization performance.
  Results: In our experiments we show that a well-trained U-network shows the
best generalization behavior and is sufficient to solve this segmentation
problem. We illustrate why extensions of this model in a realistic scenario can
be not only pointless but even harmful.
  Conclusions: We conclude from our experiments that the generalization
performance of deep neural networks is severely limited in medical image
analysis especially in the area of brain tumor segmentation. In our opinion,
current topologies are optimized for the actual benchmark data set, but are not
directly applicable in daily clinical practice.
