++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation

The ++nnU-Net introduces a novel data augmentation module utilizing image registration to enhance the nnU-Net framework for medical segmentation tasks. Evaluated on five 2D datasets, the ++nnU-Net demonstrates significant improvements in Dice Similarity Coefficient scores, with gains of up to 22% over the baseline nnU-Net. This approach addresses the challenges of data scarcity in medical imaging by generating new warped images and supplementary binary masks, offering a scalable solution for practitioners working in data-limited environments.