An attempt at beating the 3D U-Net
Fabian Isensee, Klaus Hermann Maier-Hein · 2019
The U-Net is arguably the most successful segmentation architecture in the medical domain.Here we apply a 3D U-Net to the 2019 Kidney and Kidney Tumor Segmentation Challenge and attempt to improve upon it by augmenting it with residual and pre-activation residual blocks.Cross-validation results on the training cases suggest only very minor, barely measurable improvements.Due to marginally higher dice scores, the residual 3D U-Net is chosen for test set prediction.With a Composite Dice score of 91.23 on the test set, our method outperformed all 105 competing teams and won the KiTS2019 challenge by a small margin.