MedMLX/nnunet-liver-mlx

nnU-Net · Liver & Tumor

Segment liver and tumor labels from 3D CT with nnU-Net, locally on Apple Silicon.

GitHub · Files · Docs · Paper

Input	Output
3D CT · NIfTI	Liver and 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-liver-mlx --revision 574a9c601b9b1a9e906ce985fbd46b106c5b7caa --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=['image.nii.gz'],
    output_dir='outputs',
    folds=[0],
    use_mirroring=False,
    step_fraction=0.5,
)
print(result["outputs"])

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}
}