Simplify model card with concise usage and MedMLX branding

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license: apache-2.0
library_name: mlx
tags:
  - mlx
  - native-mlx
  - apple-silicon
  - medical-imaging
  - mri
  - brain-tumor
  - segmentation
  - research
language:
  - en
inference: false
pipeline_tag: image-segmentation
base_model: MONAI/brats_mri_segmentation

SegResNet · Brain Tumor

Segment brain tumor regions from four MRI sequences with the MONAI SegResNet checkpoint.

GitHub · Files · Docs

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

Apple Silicon · Metal · Python 3.13 / 3.14

Quick start

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

repo="https://github.com/MedMLX/segresnet-mlx"
release="$repo/releases/download/v0.2.1"
uv pip install "$release/segresnet_mlx-0.2.1-py3-none-any.whl"
uv tool install huggingface-hub

hf download MedMLX/segresnet-brats-mlx --local-dir models

Replace the example input paths with your own files.

from segresnet_mlx.medmlx import run

result = run(
    bundle='brats_mri_segmentation',
    weights_path='models/segresnet-brats.safetensors',
    output_dir='outputs',
    t1c_path='T1gd.nii.gz',
    t1_path='T1w.nii.gz',
    t2_path='T2w.nii.gz',
    flair_path='FLAIR.nii.gz',
)
print(result["outputs"])

All four MRI volumes must share the same voxel grid.

Use & license

For research and education. Not validated for clinical use.

Weights: Apache-2.0. Notices.