Robustness of Brain Tumor Segmentation
This work highlights a critical gap in applying AI to medical imaging, showing that incremental improvements may not translate to real-world clinical use.
The study evaluated the generalization of deep neural networks for brain tumor segmentation in clinical practice, finding that a well-trained U-network performed best and that current methods are limited beyond benchmark datasets.
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.