TotalSegmentator-KonfAI

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

🧩 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.

⭐ 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

⚡ Efficient inference

🔬 Performance comparison

Same input, same weights (Datasets 291–295, 1.5 mm, 5-model total), same PyTorch build (2.12.1, cu13.0), single NVIDIA RTX PRO 5000 (24 GB), TotalSegmentator 2.18.0. Peak RAM = process-tree resident set; peak VRAM = over baseline. Measured with KonfAI's benchmarks/perf/bench_apps.py (2026-09-09).

📈 Key observations

• 1.8–3.9× faster whole-body inference across sizes, 2.7–4.8× less host RAM.
• KonfAI trades more VRAM on small/medium cases (larger patches, GPU accumulation) for the speed-up, while it stays inside a 24 GB card.
• On large cases the streaming reassembly bounds VRAM (10.6 GB) where the original nears the card limit (23.1 GB / 24 GB), so KonfAI is then lighter on both RAM and VRAM.

🧠 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/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/totalsegmentator

⚙️ Usage

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

Perform segmentation on an input volume:

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

Evaluate against a reference, or run both at once:

totalsegmentator-konfai eval total -i image.nii.gz --gt reference.nii.gz -o ./Output/
totalsegmentator-konfai pipeline total -i image.nii.gz --gt reference.nii.gz --gpu 0

Arguments

Note: TotalSegmentator models do not expose an uncertainty workflow, so there is no uncertainty sub-command.

📖 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

⚡ Performance & VRAM

Benchmarked on a single NVIDIA RTX PRO 5000 (24 GB) with a real whole-body CT (295 × 259 × 219, 2 mm), patch [96, 128, 160], 5-model ensemble (total), half precision (autocast). The app measures its batch size on your GPU (a forward of one patch, then of two, then the largest power of two that fits half of the free VRAM); override it in SlicerKonfAI (⚙ Advanced) or on the CLI with --patch-size / --batch-size.

The 5-model total head (117 classes) needs ~20 GB for its forward, so the ensemble targets a 24 GB card; on smaller cards use total-3mm (1 model, 3 mm). Its whole-volume accumulator is too large for the GPU, so reassembly runs on the host (~19 GB RAM). A larger batch saturates the card and slows inference. Inference scales with the case size.

🔗 Links

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