license: apache-2.0
library_name: mlx
pipeline_tag: image-segmentation
tags:
- mlx
- apple-silicon
- medical-imaging
- segmentation
- nnunet
- safetensors
- research
TotalSegmentator v2 CT for MLX
Native MLX safetensors for TotalSegmentator v2 CT inference on Apple Silicon. The five 1.5 mm standard models are in 291–295, the 3 mm fast model in 297, the 6 mm coarse model in 298, and the body crop model in 300. Each folder includes nnU-Net plans and model metadata.
The weights were converted from the official TotalSegmentator v2.0.0 weight archives for use with totalsegmentator-mlx. Conversion changes tensor names and layouts for MLX; the models were not retrained.
Quick start
uv venv --python 3.13
source .venv/bin/activate
uv pip install \
"nnunet-mlx>=0.6.4" \
"totalsegmentator-mlx>=0.1.6" \
huggingface-hub
hf download MedMLX/totalsegmentator-mlx --revision 6c03d6f90ccf53f224c3985cbdecf1eaa96555af --local-dir models
totalsegmentator-mlx predict -i ct.nii.gz -o mask.nii.gz --model-dir models
The total and body tasks are distributed by the authors under Apache-2.0. See the upstream repository and the included license for terms and attribution. Please cite TotalSegmentator and nnU-Net as described by the upstream authors. For research and education; outputs require human review.
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}
}