totalsegmentator-konfai 1.5.3

Fast and lightweight TotalSegmentator CLI powered by the KonfAI framework.

TotalSegmentator-KonfAI

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

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🧩 Overview

TotalSegmentator-KonfAI is a lightweight command-line interface (CLI) for running TotalSegmentator models for multi-organ medical image segmentation, through the KonfAI deep learning framework.

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

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⭐ Key Advantages

📦 Lightweight model distribution

• ~125 MB per model 1.5 mm model
• 🔁 Compared to ~234 MB per model for the original TotalSegmentator
• ~66.2 MB 3 mm models
• 🔁 Compared to ~135 MB (original)

➡️ Faster setup, smaller disk footprint

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⚡ Efficient inference

🔬 Performance comparison (single CT volume)

Experimental setup

• Input volume size: 512 × 512 × 366
• GPU: NVIDIA RTX 6000
• CPU: Intel® Xeon® w5-3425

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Original TotalSegmentator

Configuration	Time	Peak RAM	Peak VRAM
Total – 5 models	82.37 s	33.1 GB	~4.7 GB
Total 3 mm – 1 model	30.07 s	30.6 GB	~3.4 GB

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TotalSegmentator-KonfAI

Configuration	Time	Peak RAM	Peak VRAM
Total – 5 models	61.55 s	32.5 GB	~4.3 GB
Total 3 mm – 1 model	22.85 s	10.5 GB	~3.4 GB

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📈 Key observations

• Faster inference times compared to the original TotalSegmentator
• Significantly lower RAM usage for 3 mm models (≈ 10.5 GB vs ≈ 30.6 GB)

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🧠 Features

• ⚡ Fast inference powered by KonfAI
• 🤗 Automatic model download from Hugging Face
• 🧠 Supports evaluation workflows with reference data
• 🧾 Multi-format compatibility: supports all major medical image formats handled by ITK

🚀 Installation

From PyPI:

python -m pip install totalsegmentator-konfai

From source:

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

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⚙️ Usage

Perform segmentation on an input volume:

totalsegmentator-konfai total -i path/to/image.nii.gz -o ./Output/

Optional arguments

Flag	Description	Default
TASK	Input modality / model name on Hugging Face	total, total_mr, total_3mm, total_mr_3mm
-i, --input	Path to the input medical image	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
--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

totalsegmentator-konfai total -i path/to/input.nii.gz -o ./Output/ --gt path/to/reference.nii.gz --mask path/to/mask.nii.gz --gpu 0 -uncertainty

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📖 Reference

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

• Wasserthal, J. et al. (2023).
  TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.
  Radiology: Artificial Intelligence, 5(5). https://doi.org/10.1148/ryai.230024

• Akinci D’Antonoli, T. et al. (2025).
  TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI.
  Radiology, 314(2). https://doi.org/10.1148/radiol.241613

• 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 TotalSegmentator: github.com/wasserth/TotalSegmentator
• 🤗 Model Hub: huggingface.co/VBoussot/TotalSegmentator-KonfAI
• 📦 PyPI Package: pypi.org/project/totalsegmentator-konfai

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