nnU-Net

nnU-Net (no-new-Net) is a robust, self-configuring framework for medical image segmentation. In the 2026 AI landscape, it remains the industry standard for biomedical imaging pipelines due to its unique ability to automatically adapt the U-Net architecture to the specific properties of any dataset. Unlike traditional models that require manual tuning of hyperparameters, nnU-Net handles data fingerprinting, preprocessing, and architecture configuration (2D, 3D low-res, 3D full-res, and 3D cascade) based on the input data's resolution, voxel spacing, and intensity distributions. Its technical architecture is built on PyTorch and follows a strictly systematic approach to data augmentation and cross-validation, ensuring state-of-the-art performance across diverse modalities including MRI, CT, and microscopy. For 2026, its integration into clinical decision support systems is facilitated by its high reproducibility and a 'zero-shot' approach to pipeline generation, making it indispensable for both academic research and high-scale medical device manufacturing. It consistently outperforms manually tuned networks in international competitions (MICCAI), serving as the de facto benchmark against which all new segmentation algorithms are measured.