nnU-Net¶
nnU-Net is a semantic segmentation method that automatically adapts to a given dataset. It will analyze the provided training cases and automatically configure a matching U-Net-based segmentation pipeline.
nnU-Net is built for semantic segmentation. It can handle 2D and 3D images with arbitrary input modalities/channels. It can understand voxel spacings, anisotropies and is robust even when classes are highly imbalanced.
nnU-Net relies on supervised learning, which means that you need to provide training cases for your application. The number of required training cases varies heavily depending on the complexity of the segmentation problem.
nnU-Net expects to be able to process entire images at once during preprocessing and postprocessing, so it cannot handle enormous images.