mrsegmentator-konfai 1.5.2

Fast and lightweight MRSegmentator CLI powered by the KonfAI framework.

MRSegmentator-KonfAI

Fast and lightweight CLI for whole-body MRI segmentation using MRSegmentator models within the KonfAI framework.

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🧩 Overview

MRSegmentator-KonfAI is a lightweight command-line interface (CLI) for running
MRSegmentator models through the
KonfAI deep learning framework.

It provides fast and efficient inference for whole-body MRI segmentation, including on low-resource hardware.
Pretrained models are automatically downloaded from
Hugging Face Hub.

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🚀 Installation

From PyPI:

python -m pip install mrsegmentator-konfai

From source:

git clone https://github.com/vboussot/KonfAI.git
python -m pip install -e apps/mrsegmentator

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⚙️ Usage

Run inference on an MRI scan:

mrsegmentator-konfai -i path/to/input.nii.gz -o ./Output/

Optional arguments

Flag	Description	Default
-i, --input	Path to the input MRI volume	required
-o, --output	Path to save the segmentation	./Output/
--gt	Path to reference segmentation (ground truth), if available (enables evaluation workflows)	unset
--mask	Path to region-of-interest mask used for evaluation and uncertainty analysis	unset
-f, --folds	Number of model folds to ensemble (1–5)	2
-uncertainty	Save uncertainty maps	False
--gpu	GPU list (e.g. 0 or 0,1)	CPU if unset
--cpu	Number of CPU cores (if no GPU)	1
-q, --quiet	Suppress console output	False

Example

mrsegmentator-konfai -i path/to/input.nii.gz -o ./Output/ --gt path/to/reference.nii.gz --mask path/to/mask.nii.gz --gpu 0 -f 3 -uncertainty

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🧠 Features

• ⚡ Fast inference powered by KonfAI
• 🤗 Automatic model download from Hugging Face
• 🧩 Multi-model ensembling
• 🧠 Supports evaluation workflows with reference data, and uncertainty estimation without reference
• 🧾 Multi-format compatibility: supports all major medical image formats handled by ITK

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📖 Reference

If you use MRSegmentator-KonfAI in your work, please cite the original MRSegmentator work in addition to this CLI tool.

• Häntze, H. et al. (2025).
  Segmenting Whole-Body MRI and CT for Multiorgan Anatomic Structure Delineation.
  Radiology: Artificial Intelligence, 7(6). https://doi.org/10.1148/ryai.240777

• Boussot, V., & Dillenseger, J.-L. (2025).
  KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imaging.
  arXiv preprint arXiv:2508.09823

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🔗 Links

• 🧠 Original MRSegmentator: github.com/hhaentze/MRSegmentator
• 🤗 Model Hub: huggingface.co/VBoussot/MRSegmentator-KonfAI
• 📦 PyPI Package: pypi.org/project/mrsegmentator-konfai