---
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- CT
- abdominal
- multi-organ
- segmentation
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: validation
    path: validation.jsonl
---

# AMOS22 CT Dataset

## Dataset Description

This is the **CT** portion of the AMOS22 (A large-scale abdominal multi-organ benchmark for versatile medical image segmentation) dataset.

The AMOS22 dataset contains abdominal CT scans with dense segmentation annotations for 15 organs.

### Dataset Structure

```
dict_keys(['train', 'valid']) splits:

train/
├── imagesTr/         # CT scan images in NIfTI format (.nii.gz)
└── labelsTr/         # Segmentation masks in NIfTI format (.nii.gz)

valid/
├── imagesVa/         # CT scan images in NIfTI format (.nii.gz)
└── labelsVa/         # Segmentation masks in NIfTI format (.nii.gz)

```

### Labels

The dataset includes segmentation masks for 15 abdominal organs:
- Spleen
- Right Kidney & Left Kidney
- Gallbladder
- Esophagus
- Liver
- Stomach
- Aorta
- Inferior Vena Cava (IVC)
- Pancreas
- Right Adrenal Gland & Left Adrenal Gland
- Duodenum
- Urinary Bladder

### Data Format

- **Modality**: CT
- **Images**: NIfTI format (.nii.gz)
- **Masks**: NIfTI format (.nii.gz)

### JSONL Format

Each line in the JSONL file contains:
```json
{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ["organ1", "organ2", ...],
  "modality": "CT",
  "dataset": "AMOS22_CT",
  "official_split": "train" or "valid",
  "patient_id": "patient_id"
}
```

## Usage

```python
from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/amos22-ct-dataset")

# Access train and validation splits
train_ds = ds['train']
val_ds = ds['valid']
```

## Citation

If you use this dataset, please cite the AMOS22 challenge:

```
@article{ji2022amos,
  title={AMOS: A large-scale abdominal multi-organ benchmark for versatile medical image segmentation},
  author={Ji, Yuanfeng and Bai, Haotian and Yang, Jie and Ge, Chongjian and Zhu, Ye and Zhang, Ruimao and Li, Zhen and Zhang, Lingyan and Ma, Wanling and Wan, Xiang and others},
  journal={arXiv preprint arXiv:2206.08023},
  year={2022}
}
```

## License

CC-BY-4.0

## Dataset Homepage

https://amos22.grand-challenge.org/