| --- |
| 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") |
| ``` |
|
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|
