MedMLX/nnunet-brats-mlx

nnU-Net · Brain Tumor

Segment brain tumor regions from four MRI sequences with the nnU-Net BraTS checkpoint.

GitHub · Files · Docs · Paper

Input	Output
T1, T1ce, T2 + FLAIR · NIfTI	Brain tumor segmentation

Apple Silicon · Metal · Python 3.13

Quick start

uv venv --python 3.13
source .venv/bin/activate

uv pip install "nnunet-mlx>=0.6.4"
uv tool install huggingface-hub

hf download MedMLX/nnunet-brats-mlx --revision 7632a1f3c74d350eb6f2daeb35b43b75ef7601bd --local-dir models

Replace the example input paths with your own files.

from nnunet_mlx.medmlx import run

result = run(
    weights_path='models',
    image_paths=['T1w.nii.gz', 'T1gd.nii.gz', 'T2w.nii.gz', 'FLAIR.nii.gz'],
    output_dir='outputs',
)
print(result["outputs"])

Keep all four MRI volumes on the same voxel grid, in the order shown.

About nnunet-mlx

See the project README for package installation and current documentation.

Use & license

For research and education. Not validated for clinical use.

Weights: CC-BY-4.0. Notices.

Paper references

• Framework: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation.

Citation

If you use MedMLX, please cite the MedMLX paper:

@article{sandoval2026medmlx,
  title   = {MedMLX: On-Device Medical Imaging AI for Apple Silicon},
  author  = {Sandoval, A. and Sandoval, J. and Saleem, A.},
  journal = {medRxiv},
  year    = {2026},
  doi     = {10.64898/2026.10.28.26366762}
}