Open dataset · CT

TotalSegmentator — Pleural + Pericardial Effusion

600 CT cases with manual segmentations of pleural + pericardial effusions. Real clinical fluid pathology.

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CT 600 cases CC-BY-4.0 Reference masks Pathology
TotalSegmentator — Pleural + Pericardial Effusion — example case rendered in the MedSeg editor
Example case with its reference segmentation — straight from the catalog, rendered by MedSeg.

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8ITmAVO1fzqcKTDm — TotalSegmentator — Pleural + Pericardial Effusion case preview

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 Nv4mlOmqzCnM7arg — TotalSegmentator — Pleural + Pericardial Effusion case preview

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 01FrbE7fAKGO3mAB — TotalSegmentator — Pleural + Pericardial Effusion case preview

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 0EPSJmxG8uBYUBZ2 — TotalSegmentator — Pleural + Pericardial Effusion case preview

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 0dyMlgM3ppdGrBNv — TotalSegmentator — Pleural + Pericardial Effusion case preview

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 10Pnlj0oXaiJgZKe — TotalSegmentator — Pleural + Pericardial Effusion case preview

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About this dataset

TotalSegmentator pleural + pericardial effusion — 600 CT scans with manual segmentations of fluid collections (pleural effusion + pericardial effusion). Released by Wasserthal's group at University Hospital Basel in May 2026 as the public training data behind the TS pleural-effusion subtask model. Real clinical data, mostly emergency / oncology patients. nnU-Net v2 format.

The user-facing label kinds are surfaced as `pleural_effusion` and `pericardial_effusion` in the editor; both are common incidental findings in routine thoracic imaging.

FactValue
Cases600
Series600
Size8.9 GB
ModalityCT
Reference masksYes
LicenseCC-BY-4.0
PublisherUniversity Hospital Basel
Version2026-05
DOI10.5281/zenodo.20272295

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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