Image Segmentation
MLX
English
native-mlx
apple-silicon
medical-imaging
mri
brain-tumor
segmentation
research
Instructions to use MedMLX/segresnet-brats-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/segresnet-brats-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/segresnet-brats-mlx --local-dir segresnet-brats-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from MedMLX/segresnet-brats-mlx: direct link, hf CLI and curl.
- Browser
- Download file 1.88 kB
-
https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/README.md
- Command line
-
hf download hf://MedMLX/segresnet-brats-mlx/README.md
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curl -L -o README.md https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/README.md
1.88 kB
| 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 | |
| <img src="https://huggingface.co/MedMLX/segresnet-brats-mlx/resolve/main/banner.png" alt="MedMLX — Segment. BRAIN MRI / SEGRESNET" width="100%"> | |
| # SegResNet · Brain Tumor | |
| Segment brain tumor regions from four MRI sequences with the MONAI SegResNet checkpoint. | |
| [GitHub](https://github.com/MedMLX/segresnet-mlx) · [Files](https://huggingface.co/MedMLX/segresnet-brats-mlx/tree/main) · [Docs](https://github.com/MedMLX/segresnet-mlx/blob/main/README.md#quick-start) | |
| | Input | Output | | |
| | :--- | :--- | | |
| | T1, T1ce, T2 + FLAIR · NIfTI | Brain tumor segmentation | | |
| **Apple Silicon · Metal · Python 3.13 / 3.14** | |
| ## Quick start | |
| ```bash | |
| 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. | |
| ```python | |
| 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](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/LICENSE). [Notices](https://huggingface.co/MedMLX/segresnet-brats-mlx/blob/main/NOTICE). | |