In 3D biomedical image segmentation, dataset properties like imaging modality, image sizes, voxel spacings, class ratios etc vary drastically.
nnU-Net is the first segmentation method that is designed to deal with the dataset diversity found in the domain. It condenses and automates the keys decisions for designing a successful segmentation pipeline for any given dataset.
nnU-Net makes the following contributions to the field:
- Standardized baseline: nnU-Net is the first standardized deep learning benchmark in biomedical segmentation. Without manual effort, researchers can compare their algorithms against nnU-Net on an arbitrary number of datasets to provide meaningful evidence for proposed improvements.
- Out-of-the-box segmentation method: nnU-Net is the first plug-and-play tool for state-of-the-art biomedical segmentation. Inexperienced users can use nnU-Net out of the box for their custom 3D segmentation problem without need for manual intervention.
- Framework: nnU-Net is a framework for fast and effective development of segmentation methods. Due to its modular structure, new architectures and methods can easily be integrated into nnU-Net. Researchers can then benefit from its generic nature to roll out and evaluate their modifications on an arbitrary number of datasets in a standardized environment.