2021 IEEE International Conference on Image Processing
19-22 September 2021 • Anchorage, Alaska, USA
Imaging Without Borders
| Paper ID | BIO-1.8 | ||
| Paper Title | WHAT IS THE BEST DATA AUGMENTATION FOR 3D BRAIN TUMOR SEGMENTATION? | ||
| Authors | Marco Domenico Cirillo, David Abramian, Anders Eklund, Linköpings Universitet, Sweden | ||
| Session | BIO-1: Biomedical Signal Processing 1 | ||
| Location | Area C | ||
| Session Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation Time: | Monday, 20 September, 13:30 - 15:00 | ||
| Presentation | Poster | ||
| Topic | Biomedical Signal Processing: Medical image analysis | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | 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. Our code is available at https://github.com/mdciri/3D-augmentation-techniques | ||