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MedOtter/amos22-mri-dataset

AMOS22 MRI Dataset Dataset Description This is the MRI portion of the AMOS22 (A large-scale abdominal multi-organ benchmark for versatile medical image segmentation) dataset. The AMOS22 dataset contains abdominal MRI scans with dense segmentation annotations for 15 organs. Dataset Structure dict_keys(['train', 'valid']) splits: train/ ├── imagesTr/ # MRI scan images in NIfTI format (.nii.gz) └── labelsTr/ # Segmentation masks in… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/amos22-mri-dataset.

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AMOS22 MRI Dataset

Dataset Description

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

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

Dataset Structure

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

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

valid/
├── imagesVa/         # MRI 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: MRI
  • —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": "MRI",
  "dataset": "AMOS22_MRI",
  "official_split": "train" or "valid",
  "patient_id": "patient_id"
}

Usage

python
from datasets import load_dataset

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