AMOS22 - Abdominal Multi-Organ
500 CT + 100 MRI scans, 15 abdominal organs, multi-center / multi-vendor / multi-phase.
The sample cases below open read-only in the MedSeg editor - no account needed. Open the full dataset to copy all 600 cases into your workspace, run AI models, and segment.
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.
| Fact | Value |
|---|---|
| Cases | 600 |
| Series | 600 |
| Size | 24.2 GB |
| Modality | CT, MR |
| Reference masks | Yes |
| License | CC-BY-4.0 |
| Publisher | AMOS22 Challenge Consortium |
| Version | 2022 |
| DOI | 10.5281/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.
- Copy cases into a project.
Filter, multi-select, copy - reference masks come along if you want them. - Run AI segmentation.
TotalSegmentator, MRSegmentator, nnInteractive 3D clicks/scribbles, or text-prompted VoxTell. - Edit and measure.
Brush, lasso, fill, oblique planes, volumes in ml - in the browser. - 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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