Datasets:
metadata
license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical
- CT
- segmentation
- WORD
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
- split: test
path: test.jsonl
- split: validation
path: validation.jsonl
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