MRSegmentator-KonfAI

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

🧩 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.

⭐ Key Advantages

📦 Lightweight model distribution

• ~128 MB per model, with up to 5 folds available
• Download only the folds you need
• Total size with 5 folds: ~640 MB
• 🔁 Compared to ~1.07 GB for the original full MRSegmentator model distribution

➡️ Faster setup, smaller disk footprint

⚡ Efficient inference

🔬 Performance comparison

Same input, the five folds ensembled on both sides (-f 5), same PyTorch build (2.12.1, cu13.0), single NVIDIA RTX PRO 5000 (24 GB), MRSegmentator 2.0.0. Peak RAM = process-tree resident set; peak VRAM = over baseline. Measured with benchmarks/perf/bench_apps.py (2026-09-09).

📈 Key observations

• 1.2–1.7× faster, 1.4–5.4× less host RAM; the gap widens with the volume.
• The GPU-resident accumulator trades more VRAM for the speed and low host RAM, while streaming keeps it bounded: on the large case host RAM stays at 6.9 GB where the original grows to 37.4 GB.
• The default -f 2 ensembles two folds and runs faster than the table.

🧠 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

🚀 Installation

From PyPI:

python -m pip install mrsegmentator-konfai

From source:

git clone https://github.com/fideus-labs/KonfAI.git
cd KonfAI
# konfai and konfai-apps must come from the same checkout: this app pins both to its own
# setuptools_scm version, which only exists on PyPI at a release tag.
python -m pip install -e . -e konfai-apps -e apps/mrsegmentator

⚙️ Usage

The CLI is organised into sub-commands, mirroring the KonfAI Apps operations:

Run segmentation on an MRI scan:

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

Evaluate against a reference, or run everything at once:

mrsegmentator-konfai eval -i input.nii.gz --gt reference.nii.gz -o ./Output/
mrsegmentator-konfai pipeline -i input.nii.gz --gt reference.nii.gz --gpu 0 -f 3 -uncertainty

Arguments

📖 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

⚡ Performance & VRAM

Benchmarked on a single NVIDIA RTX PRO 5000 (24 GB) with a real whole-body MR (295 × 259 × 219, 2 mm), patch [96, 128, 160], 5-fold ensemble, half precision (autocast). The app auto-selects the batch size from your free GPU VRAM (vram_plan); override it in SlicerKonfAI (⚙ Advanced) or on the CLI with --patch-size / --batch-size.

On a 24 GB card the accumulator stays on the GPU, keeping host RAM low with a byte-identical result. The plan stops short of filling the card: a still-larger batch (12 → ~24 GB) saturates the allocator and slows inference ~2× without running faster. Inference scales with the case size.

🔗 Links

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