nnU-Net Automated Medical Image Segmentation

Overview

nnU-Net (no-new-Net) is a self-configuring deep learning framework for biomedical image segmentation. Given a labeled training dataset, nnU-Net automatically determines the optimal network architecture (2D, 3D full-resolution, or 3D cascade), preprocessing steps (resampling, normalization, patch size), training schedule, and post-processing. It consistently achieves state-of-the-art performance across diverse imaging modalities and anatomical structures without manual hyperparameter tuning. nnU-Net v2 (nnunetv2) is the current release with a Python API for inference alongside the standard CLI.

When to Use

• Segmenting anatomical structures in CT or MRI scans (organs, tumors, lesions) when you have 20+ annotated training cases
• Automating cell or nucleus segmentation in 3D fluorescence or electron microscopy volumes
• Establishing a strong baseline for any new segmentation challenge without manually tuning a U-Net
• Running inference on new images using a pretrained nnU-Net model from a published challenge
• Comparing segmentation methods: nnU-Net's auto-configured ensembles serve as a rigorous baseline
• Building production segmentation pipelines where training is done once and inference is repeated on many new cases
• Use Cellpose (cellpose-cell-segmentation) instead for 2D fluorescence cell segmentation without labeled training data; nnU-Net requires annotated training cases
• Use SimpleITK (simpleitk-image-registration) instead for rule-based segmentation with classical thresholding and region growing on images where deep learning training data is unavailable

Prerequisites