---
license: cc-by-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- medical
- image
- ct
- mri
- abdomen
- segmentation
- detection
pretty_name: 'amos22-lite'
size_categories:
- n<1K
---


## About
This is a preprocessed redistribution of [AMOS22](https://amos22.grand-challenge.org) ([Zenodo](https://zenodo.org/records/7262581)), which is released under the `CC BY 4.0` license.

**Dataset summary:** 360 abdominal scans (300 CT + 60 MRI) with 15-class multi-organ segmentation masks.

**Contents of this repository:**

- `AMOS22-CT/Images/` — 300 files
- `AMOS22-CT/Masks/` — 300 files
- `AMOS22-MRI/Images/` — 60 files
- `AMOS22-MRI/Masks/` — 60 files

📝 Landmark annotations, visualization figures and the benchmark plan files live in 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can load the complete images and annotations from dataset configs.


## Relation to the source dataset

| | |
| --- | --- |
| In the source | the AMOS 2022 release - abdominal CT and MRI across train/val/test; expert masks are public for the train+val split only (300 CT + 60 MRI) |
| Excluded here | the unlabelled test images (`imagesTs`), which ship no public masks |
| **In this repo** | **300 `AMOS22-CT/Images` + 300 `AMOS22-CT/Masks` + 60 `AMOS22-MRI/Images` + 60 `AMOS22-MRI/Masks`** |

**Every labelled case is included - 300 CT and 60 MRI.** No format conversion was required: the source already ships `nii.gz`, and the image voxel data is carried over unmodified. What is derived here is the RAS+ reorientation and the modality-split layout that replaces the source's `imagesTr`/`imagesVa` + `labelsTr`/`labelsVa` folders.

CT and MRI are shipped as **separate** folder pairs because MedVision treats them as two tasks, and because the source encodes the modality only in the case number (`amos_0001`-`amos_0506` are CT, `amos_0507`+ are MRI) - a fact that is easy to lose once the files are pooled.

**Why `-Lite`?** The suffix marks this as a *derived* redistribution rather than a copy of the source. These are **preprocessed** volumes — every case has been format-converted where needed, geometry-normalised and reoriented to RAS+ — and for some sources cases or modalities are excluded as well (see the table above). Use it to reproduce MedVision, not as a substitute for the original release. See [Preprocessing](#preprocessing) below for exactly what was changed.


## Preprocessing

- Built from the single official archive `amos22.zip` (`https://zenodo.org/records/7155725/files/amos22.zip`).

- `imagesTr` + `imagesVa` and `labelsTr` + `labelsVa` are pooled and then split by case number into `AMOS22-CT/` (`amos_0001`-`amos_0506`) and `AMOS22-MRI/` (`amos_0507`+). Filenames keep the source stem, so a case ID is identical across `Images/` and `Masks/`.

- No format conversion and no resampling - the source is already `nii.gz`, and the 15-label mask encoding is the source's. Only the orientation is standardized to RAS+.


## Segmentation Labels

```python
labels_map = {
    "1": "spleen",
    "2": "right kidney",
    "3": "left kidney",
    "4": "gall bladder",
    "5": "esophagus",
    "6": "liver",
    "7": "stomach",
    "8": "aorta",
    "9": "postcava",
    "10": "pancreas",
    "11": "right adrenal gland",
    "12": "left adrenal gland",
    "13": "duodenum",
    "14": "bladder",
    "15": "prostate/uterus"
}
```


## News
- [2 Aug, 2026] Initial release. This dataset is integrated into 🔥[MedVision](https://huggingface.co/datasets/YongchengYAO/MedVision)🔥, where you can use these config names to load data in python:

  - `AMOS22_BoxSize_Task01_Axial_Test`
  - `AMOS22_BoxSize_Task01_Axial_Train`
  - `AMOS22_BoxSize_Task01_Coronal_Test`
  - `AMOS22_BoxSize_Task01_Coronal_Train`
  - `AMOS22_BoxSize_Task01_Sagittal_Test`
  - `AMOS22_BoxSize_Task01_Sagittal_Train`
  - `AMOS22_BoxSize_Task02_Axial_Test`
  - `AMOS22_BoxSize_Task02_Axial_Train`
  - `AMOS22_BoxSize_Task02_Coronal_Test`
  - `AMOS22_BoxSize_Task02_Coronal_Train`
  - `AMOS22_BoxSize_Task02_Sagittal_Test`
  - `AMOS22_BoxSize_Task02_Sagittal_Train`
  - `AMOS22_MaskSize_Task01_Axial_Test`
  - `AMOS22_MaskSize_Task01_Axial_Train`
  - `AMOS22_MaskSize_Task01_Coronal_Test`
  - `AMOS22_MaskSize_Task01_Coronal_Train`
  - `AMOS22_MaskSize_Task01_Sagittal_Test`
  - `AMOS22_MaskSize_Task01_Sagittal_Train`
  - `AMOS22_MaskSize_Task02_Axial_Test`
  - `AMOS22_MaskSize_Task02_Axial_Train`
  - `AMOS22_MaskSize_Task02_Coronal_Test`
  - `AMOS22_MaskSize_Task02_Coronal_Train`
  - `AMOS22_MaskSize_Task02_Sagittal_Test`
  - `AMOS22_MaskSize_Task02_Sagittal_Train`


## Data Usage Agreement
By using the dataset, you agree to the terms as follow.
- You must comply with the original `CC BY 4.0` license terms of the source dataset.
- You are recommended to refer to the source of this dataset in any publication: `https://huggingface.co/datasets/YongchengYAO/AMOS22-Lite`
- You must cite the original publication(s):
  - https://doi.org/10.48550/arXiv.2206.08023


## Official Release
For more information, please go to the official site: https://amos22.grand-challenge.org


## Download from Huggingface
```python
# python
from huggingface_hub import snapshot_download
snapshot_download(repo_id="YongchengYAO/AMOS22-Lite", repo_type='dataset', local_dir="/your/local/folder")
```