MedOtter/chaos-ct

CHAOS CT Dataset

Dataset Description

The CHAOS CT dataset from the CHAOS (Combined Healthy Abdominal Organ Segmentation) challenge. This dataset contains CT scans for liver segmentation from CT scans.

Dataset Details

• Modality: CT
• Target: liver
• Format: NIfTI (.nii.gz)
• Challenge: CHAOS 2019

Dataset Structure

Each sample in the JSONL file contains:

{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ['liver'],
  "modality": "CT",
  "dataset": "CHAOS_CT",
  "official_split": "train",
  "patient_id": "patient_id"
}

Organ Labels

• Liver: Single organ segmentation

Usage

Load Metadata

from datasets import load_dataset

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

# 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="Angelou0516/chaos-ct",
    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{kavur2021chaos,
  title={CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation},
  author={Kavur, A Emre and Gezer, N Sinem and Bar\i c, Mustafa and others},
  journal={Medical Image Analysis},
  volume={69},
  pages={101950},
  year={2021},
  publisher={Elsevier}
}

License

CC-BY-4.0

Dataset Homepage

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

Notes

• This dataset is part of the CHAOS 2019 challenge
• CT scans focus on liver segmentation
• All images are in NIfTI format