nnU-Net for Brain Tumor Segmentation
This work addresses brain tumor segmentation for medical imaging, achieving incremental improvements over an existing method.
The paper tackled brain tumor segmentation in the BraTS 2020 challenge by applying and modifying nnU-Net, achieving first place with Dice scores of 88.95, 85.06, and 82.03 for whole tumor, tumor core, and enhancing tumor, respectively.
We apply nnU-Net to the segmentation task of the BraTS 2020 challenge. The unmodified nnU-Net baseline configuration already achieves a respectable result. By incorporating BraTS-specific modifications regarding postprocessing, region-based training, a more aggressive data augmentation as well as several minor modifications to the nnUNet pipeline we are able to improve its segmentation performance substantially. We furthermore re-implement the BraTS ranking scheme to determine which of our nnU-Net variants best fits the requirements imposed by it. Our final ensemble took the first place in the BraTS 2020 competition with Dice scores of 88.95, 85.06 and 82.03 and HD95 values of 8.498,17.337 and 17.805 for whole tumor, tumor core and enhancing tumor, respectively.