BraTS algorithms
Project description
BraTS
Providing the top performing algorithms from the Brain Tumor Segmentation (BraTS) challenges, through an easy to use Python API powered by docker.
Features
- Access to top-performing algorithms from recent BraTS challenges
- Easy-to-use minimal API
- Extensive documentation and examples
Installation
With a Python 3.8+ environment, you can install brats
directly from PyPI:
pip install brats
Docker and NVIDIA Container Toolkit Setup
To run brats
you need a working Docker installation.
Most algorithms also require GPU support (NVIDIA Docker).
Installation instructions:
- Docker: Installation instructions on the official website
- NVIDIA Container Toolkit: Refer to the NVIDIA install guide and the official GitHub page
Use Cases and Tutorials
A minimal example to create a segmentation could look like this:
from brats import AdultGliomaSegmenter
segmenter = AdultGliomaSegmenter()
segmenter.infer_single(
t1c="path/to/t1c.nii.gz",
t1n="path/to/t1n.nii.gz",
t2f="path/to/t2f.nii.gz",
t2w="path/to/t2w.nii.gz",
output_file="segmentation.nii.gz",
)
For more examples and details please refer to our extensive Notebook tutorials [TODO]
Contributing
We welcome contributions from the community, including bug reports, feature requests, and code contributions. For more information on how to contribute, please see our CONTRIBUTING.md file.
Project details
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