nnU-Net is a PyTorch-based deep learning framework for the supervised semantic segmentation of 2D and 3D biomedical images. It functions as an automated medical imaging pipeline that generates predicted masks and labels from clinical images.
The system distinguishes itself by using dataset-driven auto-configuration to automatically select the optimal network architecture, preprocessing steps, and training hyperparameters based on the specific properties of the input medical dataset.
The framework covers a broad range of capabilities including medical dataset preparation, intensity normalization, and supervised segmentation training. It incorporates specialized training features such as sparse annotation handling and region-based label optimization, alongside an inference engine that utilizes sliding-window execution. Evaluation tools are provided for benchmarking both hardware performance and model segmentation accuracy.
