Add dataset card

README.md

@@ -0,0 +1,130 @@
+---
+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")
+```
+