Image Segmentation
MLX
English
native-mlx
apple-silicon
medical-imaging
ct
segmentation
vista3d
research
Instructions to use MedMLX/vista3d-ct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MedMLX/vista3d-ct-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download MedMLX/vista3d-ct-mlx --local-dir vista3d-ct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
VISTA-3D · CT Segmentation
Run automatic whole-volume CT segmentation with NVIDIA NV-Segment-CT and native MLX.
| Input | Output |
|---|---|
| 3D CT · NIfTI | Anatomical 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/mlx-segment-ct"
release="$repo/releases/download/v0.2.1"
uv pip install "$release/mlx_segment_ct-0.2.1-py3-none-any.whl"
uv tool install huggingface-hub
hf download MedMLX/vista3d-ct-mlx --local-dir models
Replace the example input paths with your own files.
from mlx_segment_ct.medmlx import run
result = run(
image_path='ct.nii.gz',
weights_path='models',
output_dir='outputs',
segment_all=True,
)
print(result["outputs"])
Use & license
For research and education. Not validated for clinical use.
Weights: NVIDIA Open Model License. Notices.
Hardware compatibility
Log In to add your hardware
Quantized
Model tree for MedMLX/vista3d-ct-mlx
Base model
nvidia/NV-Segment-CT