Medical Image Segmentation with nnU-Net

Medical image segmentation assigns labels to pixels or voxels, such as outlining an organ or lesion. nnU-Net is a self-configuring framework that adapts preprocessing, network configuration, training, and postprocessing to dataset characteristics. The original study showed that systematic configuration can produce strong results across biomedical segmentation benchmarks. It is a framework for building models, not a guarantee that a model is clinically valid for every image or task. Segmentation quality depends on imaging modality, anatomy, acquisition protocol, label definitions, and agreement among annotators. Small changes in spacing, orientation, or intensity normalization can affect predictions. A model trained on one institution’s data may produce incomplete masks or include irrelevant structures at another site. Automated masks should be reviewed by qualified users before they inform clinical decisions. Researchers should define the structure and intended use, verify annotation quality, and evaluate with held-out patients and external datasets. Metrics such as Dice overlap do not fully describe whether errors matter clinically; surface distance and task-specific review may also be needed. Report preprocessing, training data, postprocessing, and failure cases. nnU-Net can streamline baseline development, but image review, independent validation, and clinical integration remain essential. Review failed cases with domain experts and distinguish segmentation quality from downstream diagnosis or treatment performance. A model mask can be technically accurate yet not improve the clinical workflow it is intended to support.