WORD (Whole abdominal ORgan Dataset) Dataset

Dataset Description

The WORD (Whole abdominal ORgan Dataset) dataset for abdominal organ segmentation with 16 organs. This dataset contains CT scans with dense segmentation annotations.

Dataset Details

• Modality: CT
• Target: liver, spleen, kidneys, stomach, gallbladder, esophagus, pancreas, duodenum, colon, intestine, adrenal gland, rectum, bladder, femoral heads
• 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": "WORD",
  "official_split": "train",
  "patient_id": "patient_id"
}

Usage

Load Metadata

from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/word")

# 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/word",
    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{word,
  title={WORD: A Large Scale Dataset for Whole Abdominal Organ Segmentation},
  year={2023}
}

License

CC-BY-4.0

Dataset Homepage

https://github.com/HiLab-git/WORD