MedMLX — Segment. RENAL CECT / SEGRESNET

SegResNet · Renal CECT

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

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

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-renal-cect-mlx --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.

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MLX

Hardware compatibility

Quantized

Model tree for MedMLX/segresnet-renal-cect-mlx

Base model

MONAI/renalStructures_CECT_segmentation

Finetuned

(1)

this model