FLARE22 Dataset

Dataset Description

The FLARE22 dataset for fast and low-resource abdominal organ segmentation. This dataset contains CT scans with dense segmentation annotations.

Dataset Details

• Modality: CT
• Target: 13 abdominal organs
• Format: NIfTI (.nii.gz)

Dataset Structure

Each sample in the JSONL file contains:

{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ["organ1", "organ2", ...],
  "modality": "CT",
  "dataset": "FLARE22",
  "official_split": "train",
  "patient_id": "patient_id"
}

Usage

Load Metadata

from datasets import load_dataset

# Load the dataset
ds = load_dataset("MedOtter/FLARE22")

# Access a sample
sample = ds['train'][0]
print(f"Patient ID: {sample['patient_id']}")
print(f"Image: {sample['image']}")
print(f"Mask: {sample['mask']}")
print(f"Labels: {sample['label']}")

Load Images

from huggingface_hub import snapshot_download
import nibabel as nib
import os

# Download the full dataset
local_path = snapshot_download(
    repo_id="MedOtter/FLARE22",
    repo_type="dataset"
)

# Load a sample
sample = ds['train'][0]
image = nib.load(os.path.join(local_path, sample['image']))
mask = nib.load(os.path.join(local_path, sample['mask']))

# Get numpy arrays
image_data = image.get_fdata()
mask_data = mask.get_fdata()

print(f"Image shape: {image_data.shape}")
print(f"Mask shape: {mask_data.shape}")

Citation

@article{flare22,
  title={FLARE 2022: Fast and Low GPU Memory Abdominal oRgan sEgmentation},
  year={2023}
}

Dataset Homepage

https://flare22.grand-challenge.org/

License

CC BY-SA 4.0 images and pancreas labels (via MSD) + AbdomenCT-1K organ labels 'research purpose only'; Zenodo 7860267 labels the 50-case release CC BY 4.0

Redistribution is permitted for non-commercial research use under the terms below; commercial use is not.

Source of the terms: https://zenodo.org/records/7860267

Audit note (verbatim from the MedOtter dataset card):

2026-09-06 adjudication (settles the 2026-08-17 conditional): the organizers' own Zenodo record 7860267 publishes the 50 labeled training cases (access_right open, licence label cc-by-4.0) and its description states 'The CT images and pancreas annotations are from http://medicaldecathlon.com/ The other organ annotations are from AbdomenCT-1K (research purpose only)'; medicaldecathlon.com licenses its data CC BY-SA 4.0 ('allowing for data to be shared, distributed and improved upon'). The mirror carries exactly those 50 cases (50 images + 50 labels, verified 2026-09-06), so re-hosting rests on the organizers' public release, the images keep CC BY-SA and the organ labels keep the research-only notice: redistribution yes_noncommercial, commercial use no. Supersedes the audit CSV row (CC BY 4.0 at face value) and the 2026-08-17 conditional.

Please cite:

@article{flare2022,
  title={FLARE 2022: Fast and Low-resource semi-supervised Abdominal oRgan sEgmentation},
  author={Ma, Jun and others},
  year={2022}
}