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BraTS algorithms

Project description

PyPI version brats-algorithms Documentation Status tests

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:

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]

Algorithms

Challenge Year Position Authors Paper CPU support
Meningioma Segmentation 2023 1st Andriy Myronenko, et al. N/A :heavy_multiplication_x:
2023 2nd Ziyan Huang N/A :white_check_mark:
2023 3rd Zhifan Jiang et al. N/A :heavy_multiplication_x:
Pediatric Segmentation 2023 1st Zhifan Jiang et al. N/A :heavy_multiplication_x:
2023 2nd Andriy Myronenko, et al. N/A :heavy_multiplication_x:
2023 3rd Yubo Zhou N/A :heavy_multiplication_x:
Adult Glioma Segmentation 2023 1st André Ferreira, et al. Link :heavy_multiplication_x:
2023 2nd Andriy Myronenko, et al. N/A :heavy_multiplication_x:
2023 3rd Fadillah Adamsyah Maani, et al. N/A :heavy_multiplication_x:

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.

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