Robustness of brain tumor segmentation

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 this 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 these 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 these 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.