An attempt at beating the 3D U-Net

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

Look Ma, no code: fine tuning nnU-Net for the AutoPET II challenge by only adjusting its JSON plans

We participate in the AutoPET II challenge by modifying nnU-Net only through its easy to understand and modify 'nnUNetPlans.json' file. By switching to a UNet with residual encoder, increasing the batch size and increasing the patch size we obtain a configuration that substantially outperforms the automatically configured nnU-Net baseline (5-fold cross-validation Dice score of 65.14 vs 33.28) at the expense of increased compute requirements for model training. Our final submission ensembles the two most promising configurations. At the time of submission our method ranks first on the preliminary test set