What is the best data augmentation for 3D brain tumor segmentation?

Understand

Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.

• Despite this fact, data augmentation has in our opinion not been fully explored for brain tumor segmentation.
• In this project we apply different types of data augmentation (flipping, rotation, scaling, brightness adjustment, elastic deformation) when training a standard 3D U-Net, and demonstrate that augmentation significantly improves the network's performance in many cases.
• Our conclusion is that brightness augmentation and elastic deformation work best, and that combinations of different augmentation techniques do not provide further improvement compared to only using one augmentation technique.