Introduction to nnUNet

nnUNet is an open-source framework designed for medical image segmentation, providing a robust solution for both 2D and 3D image processing. With its flexible architecture, nnUNet allows developers to create custom image readers tailored to specific medical imaging needs.

Main Features of nnUNet

• Support for hierarchical labels in segmentation tasks.
• Cross-platform compatibility including CUDA and Apple M1/M2 support.
• Unified trainer class for simplified training processes.
• Extensible I/O formats through custom adapters.
• Native support for multi-GPU training.

Technical Architecture and Implementation

The architecture of nnUNet is designed to be modular and extensible. At its core, it utilizes a base class called BaseReaderWriter for implementing custom image readers. Developers can derive their own adapters from this base class and reimplement the necessary abstract methods to support various input image formats.

To ensure compatibility with both 2D and 3D images, developers must handle the input appropriately or raise errors if unsupported formats are detected. Once the custom reader is implemented, it must be added to the LIST_OF_IO_CLASSES in reader_writer_registry.py for nnUNet to recognize it.

Setup and Installation Process

To get started with nnUNet, follow these steps:

1. Clone the repository from GitHub: git clone https://github.com/MIC-DKFZ/nnUNet.git
2. Navigate to the project directory: cd nnUNet
3. Install the required dependencies using pip: pip install -r requirements.txt
4. Set up the environment variables as per the documentation.

Usage Examples and API Overview

Once installed, you can start using nnUNet for your segmentation tasks. Here’s a simple example of how to train a model:

nnUNetv2_train -device 0 -fold 0 -config 3d_fullres

For predictions, you can use:

nnUNetv2_predict -device 0 -num_parts 1 -part_id 0

Refer to the official documentation for a comprehensive API overview and additional usage examples.

Community and Contribution Aspects

nnUNet is an open-source project, and contributions are welcome! Developers can contribute by submitting issues, feature requests, or pull requests on the GitHub repository. Engaging with the community through discussions and sharing experiences can also enhance the project’s development.

License and Legal Considerations

nnUNet is licensed under the Apache License 2.0. This allows for both personal and commercial use, provided that the terms of the license are followed. Make sure to review the license details to understand your rights and obligations.

Project Roadmap and Future Plans

The nnUNet team is continuously working on enhancing the framework. Upcoming features include:

• Integration with MONAI for improved functionality.
• New pretrained weights for various datasets.
• Further enhancements to the API for better usability.

Conclusion

nnUNet stands out as a powerful tool for medical image segmentation, offering flexibility and extensibility for developers. By creating custom image readers, you can tailor the framework to meet specific project requirements. Join the community and contribute to the ongoing development of this innovative project!

Learn More

For more information, visit the official GitHub repository: nnUNet GitHub Repository.

Frequently Asked Questions