Open dataset · CT

TotalSegmentator — Liver Lesions (CT)

842 CT cases with manual liver-lesion segmentations. Real clinical oncology cohort.

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CT 842 cases CC-BY-4.0 Reference masks Pathology
TotalSegmentator — Liver Lesions (CT) — example case rendered in the MedSeg editor
Example case with its reference segmentation — straight from the catalog, rendered by MedSeg.

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86FNMi2QiFmquhyj — TotalSegmentator — Liver Lesions (CT) case preview

86FNMi2QiFmquhyjOpen preview

 LIXccrDJ5ADyoANq — TotalSegmentator — Liver Lesions (CT) case preview

LIXccrDJ5ADyoANqOpen preview

 038lIVt2WjmwWJc6 — TotalSegmentator — Liver Lesions (CT) case preview

038lIVt2WjmwWJc6Sign up to open

 0AbJHdnCKYU14XnY — TotalSegmentator — Liver Lesions (CT) case preview

0AbJHdnCKYU14XnYSign up to open

 0LHth9cfqxoisxhI — TotalSegmentator — Liver Lesions (CT) case preview

0LHth9cfqxoisxhISign up to open

 0R65xRVtCrSfrJQC — TotalSegmentator — Liver Lesions (CT) case preview

0R65xRVtCrSfrJQCSign up to open

About this dataset

TotalSegmentator Liver Lesions — 842 CT cases with manual liver lesion segmentations (HCC, metastases, and other focal liver lesions). Wasserthal et al. 2026 training dataset for the TS liver-lesion subtask model. Real clinical oncology data from University Hospital Basel and partner sites. nnU-Net v2 format.

FactValue
Cases842
Series842
Size39.0 GB
ModalityCT
Reference masksYes
LicenseCC-BY-4.0
PublisherUniversity Hospital Basel
Version2026-05
DOI10.5281/zenodo.20272572

What you can do with it in MedSeg

Copied cases behave like normal project series — the public image bytes are linked, not duplicated, so copies are instant and take no extra storage.

  1. Copy cases into a project.
    Filter, multi-select, copy — reference masks come along if you want them.
  2. Run AI segmentation.
    TotalSegmentator, MRSegmentator, nnInteractive 3D clicks/scribbles, or text-prompted VoxTell.
  3. Edit and measure.
    Brush, lasso, fill, oblique planes, volumes in ml — in the browser.
  4. Train your own nnU-Net.
    Correct masks on a handful of cases and train a custom model on hosted GPUs.

Citation

If you use this dataset in your research, cite the source per its license terms.

Wasserthal, J., et al. (2026). TotalSegmentator subtask training datasets. Zenodo.

License: CC-BY-4.0

Open data still carries obligations — attribution at minimum. Check the license terms and the source publication before publishing work built on this dataset. MedSeg is a research tool, not a medical device.

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