MedMLX/segresnet-renal-cect-mlx

SegResNet · Renal CECT

Segment renal structures from three-phase contrast-enhanced CT with native MLX.

GitHub · Files · Docs · Paper

Input	Output
Arterial, venous + excretory CT	Five renal structure labels

Apple Silicon · Metal · Python 3.13

Quick start

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

uv pip install "segresnet-mlx>=0.3.1"
uv tool install huggingface-hub

hf download MedMLX/segresnet-renal-cect-mlx --revision fa67ec6dca88f1dbbf6f52d6061b34ddefe45057 --local-dir models

Replace the example input paths with your own files.

from segresnet_mlx.medmlx import run

result = run(
    bundle='renalStructures_CECT_segmentation',
    arterial_path='arterial.nii.gz',
    venous_path='venous.nii.gz',
    excretory_path='excretory.nii.gz',
    weights_path='models/segresnet-renal-cect.safetensors',
    output_dir='outputs',
)
print(result["outputs"])

All three CT phases must share the same voxel grid.

Use & license

For research and education. Not validated for clinical use.

Weights: Apache-2.0. Notices.

Paper references

• SegResNet architecture: 3D MRI Brain Tumor Segmentation Using Autoencoder Regularization.

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}
}