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TotalSegmentator

Free#18 of 33 in AI Image Segmentation Software

TotalSegmentator: A research-focused toolkit for automating CT and MRI segmentation across several workflows. Ranked #18 of 33 in AI Image Segmentation Software by our editors (7.5/10); pricing: Free plan; best for researchers automating whole-body CT segmentation.

7.5/10Editor score
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TotalSegmentator7.5 Try TotalSegmentator

At a glance

  • Editor score
    7.5 / 10
  • Pricing
    Free plan
  • Best for
    Researchers automating whole-body CT segmentation
  • Free plan
    Yes
  • Paid from
    None
  • Model training
    Yes
  • Facts checked
    28 Sep 2026

  • Where it wins

    • Whole-body CT task segments 117 classes; MRI task covers 50 classes.
    • Offers command-line, Python, Docker, and web workflows.
    • Can produce statistics, radiomics, previews, and DICOM segmentation output.

  • Where it doesn't

    • The hosted prototype is research-only, not for diagnosis or other medical use.
    • Web uploads are limited to one anonymized CT/MR dataset up to 300 MB.
    • Some model tasks require separate licensing for commercial use.

Our verdict on TotalSegmentator

TotalSegmentator is an open-source medical-imaging toolkit that automatically segments anatomical structures in CT and MRI data. Its whole-body CT task covers 117 classes, and its MRI task covers 50. Researchers and technical users can run it through a command-line interface, Python API, Docker container, or hosted web service. The hosted prototype is explicitly intended for research, not diagnosis or other medical use, so clinical users seeking a diagnostic tool should look elsewhere.

Its breadth of workflows makes it adaptable to research pipelines: the command-line interface offers selectable tasks and regions of interest, while the Python API accepts file paths or NIfTI objects. Docker supports container-based distribution, and the web workflow accepts anonymized CT or MR data. Outputs can include statistics, radiomics, previews, and DICOM segmentation. Training and evaluation guidance uses nnU-Net, making the project relevant to teams working with model development as well as pretrained segmentation. The web service limits uploads to one anonymized dataset up to 300 MB.

TotalSegmentator is free and open source, with cloud and self-hosted deployment options. Its published scope includes PyTorch and nnU-Net, and support is through documentation and the community. That combination suits research groups that can work with technical interfaces and manage their own imaging workflows. Commercial users should check licensing carefully: some model tasks require separate commercial-use licensing. Choose it for research-oriented segmentation and pipeline flexibility; avoid relying on the hosted prototype for medical decisions, or treating its open-source status as blanket commercial permission.

TotalSegmentator pricing

Plans Free planFree Free to use — no paid tier required for the core job.
See plans on totalsegmentator.com

TotalSegmentator fact sheet

Free planYes
Paid fromNone
Semantic segmentationYes
Instance segmentationNot verified
Video segmentationNot verified
Model trainingYes
Deployment optionsHybrid
API accessYes
Supported frameworksPyTorch; nnU-Net
DeploymentCloud, Self-hosted
PlatformsWeb, Windows, macOS, Linux
SupportCommunity, Docs
Built forSmall business, Mid-market, Enterprise (editorial estimate)
PricingFree plan
Websitetotalsegmentator.com
Facts checked28 Sep 2026

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Featured on iTechGuides

Featured on iTechGuides — TotalSegmentator 7.5/10

TotalSegmentator is listed in our AI Image Segmentation Software directory. Add the badge to your site — it links back to this page.

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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