Open dataset · CT, MR

AMOS22 — Abdominal Multi-Organ

500 CT + 100 MRI scans, 15 abdominal organs, multi-center / multi-vendor / multi-phase.

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CT, MR 600 cases CC-BY-4.0 Reference masks Pathology
AMOS22 — Abdominal Multi-Organ — example case rendered in the MedSeg editor
Example case with its reference segmentation — straight from the catalog, rendered by MedSeg.

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amos_0357 — AMOS22 — Abdominal Multi-Organ case preview

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 amos_0016 — AMOS22 — Abdominal Multi-Organ case preview

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 amos_0001 — AMOS22 — Abdominal Multi-Organ case preview

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 amos_0004 — AMOS22 — Abdominal Multi-Organ case preview

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 amos_0005 — AMOS22 — Abdominal Multi-Organ case preview

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 amos_0006 — AMOS22 — Abdominal Multi-Organ case preview

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About this dataset

AMOS is a large-scale, diverse, clinical dataset for abdominal organ segmentation that provides 500 CT and 100 MRI scans collected from multi-center, multi-vendor, multi-modality, multi-phase, multi-disease patients, each with voxel-level annotations of 15 abdominal organs (spleen, kidneys, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, pancreas, adrenal glands, duodenum, bladder, and prostate/uterus).

Cases come from multiple sites and scanners — the heterogeneity makes this an excellent benchmark for robustness, and the CT+MR pairing lets you compare performance across modalities on the same anatomy.

FactValue
Cases600
Series600
Size24.2 GB
ModalityCT, MR
Reference masksYes
LicenseCC-BY-4.0
PublisherAMOS22 Challenge Consortium
Version2022
DOI10.5281/zenodo.7262581

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.

Ji, Y., Bai, H., Yang, J., Ge, C., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., Wan, X., & Luo, P. (2022). AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation. NeurIPS Datasets and Benchmarks Track.

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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