Datasets:
metadata
license: cc-by-sa-4.0
task_categories:
- image-segmentation
tags:
- medical
- MRI
- prostate
- segmentation
- Medical Segmentation Decathlon
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: train.jsonl
Medical Segmentation Decathlon: Prostate
Dataset Description
This is the Prostate dataset from the Medical Segmentation Decathlon (MSD) challenge. The dataset contains MRI scans with segmentation annotations for prostate segmentation.
Dataset Details
- Modality: MRI
- Task: Task05_Prostate
- Target: peripheral zone and transition zone
- 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": "MRI",
"dataset": "MSD_Prostate",
"official_split": "train",
"patient_id": "patient_id"
}
Data Organization
Task05_Prostate/
├── 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-prostate")
# 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-prostate",
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