Angelou0516/msd-pancreas

Medical Segmentation Decathlon: Pancreas

Dataset Description

This is the Pancreas dataset from the Medical Segmentation Decathlon (MSD) challenge. The dataset contains CT scans with segmentation annotations for pancreas and pancreatic tumor segmentation.

Dataset Details

• Modality: CT
• Task: Task07_Pancreas
• Target: pancreas and pancreatic tumor
• 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": ["label1", "label2", ...],
  "modality": "CT",
  "dataset": "MSD_Pancreas",
  "official_split": "train",
  "patient_id": "patient_id"
}

Data Organization

Task07_Pancreas/
├── imagesTr/          # Training images
│   └── *.nii.gz
└── labelsTr/          # Training labels
    └── *.nii.gz

Usage

Load Metadata

from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/msd-pancreas")

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

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/msd-pancreas",
    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

If you use this dataset, please cite the Medical Segmentation Decathlon paper:

@article{antonelli2022medical,
  title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms},
  author={Antonelli, Michela and Reinke, Annika and Bakas, Spyridon and others},
  journal={Nature Communications},
  volume={13},
  number={1},
  pages={1--13},
  year={2022},
  publisher={Nature Publishing Group}
}

License

CC-BY-SA-4.0

Dataset Homepage

http://medicaldecathlon.com/