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MedOtter/msd-hippocampus

Medical Segmentation Decathlon: Hippocampus Dataset Description This is the Hippocampus dataset from the Medical Segmentation Decathlon (MSD) challenge. The dataset contains MRI scans with segmentation annotations for hippocampus segmentation. Dataset Details Modality: MRI Task: Task04_Hippocampus Target: anterior and posterior hippocampus Format: NIfTI (.nii.gz) Dataset Structure Each sample in the JSONL file contains: { "image":… See the full description on the dataset page: https://huggingface.co/datasets/MedOtter/msd-hippocampus.

sourceHugging Facecc-by-sa-4.0updated 11mo agoView on Hugging Face
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Medical Segmentation Decathlon: Hippocampus

Dataset Description

This is the Hippocampus dataset from the Medical Segmentation Decathlon (MSD) challenge. The dataset contains MRI scans with segmentation annotations for hippocampus segmentation.

Dataset Details

  • —Modality: MRI
  • —Task: Task04_Hippocampus
  • —Target: anterior and posterior hippocampus
  • —Format: NIfTI (.nii.gz)

Dataset Structure

Each sample in the JSONL file contains:

json
{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ["label1", "label2", ...],
  "modality": "MRI",
  "dataset": "MSD_Hippocampus",
  "official_split": "train",
  "patient_id": "patient_id"
}

Data Organization

Task04_Hippocampus/
├── imagesTr/          # Training images
│   └── *.nii.gz
└── labelsTr/          # Training labels
    └── *.nii.gz

Usage

Load Metadata

python
from datasets import load_dataset

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

# 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

python
from huggingface_hub import snapshot_download
import nibabel as nib
import os

# Download the full dataset
local_path = snapshot_download(
    repo_id="Angelou0516/msd-hippocampus",
    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:

bibtex
@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/