MedOtter/isles2022

{
  "image": "path/to/image.nii.gz",
  "mask": "path/to/mask.nii.gz",
  "label": ["organ1", "organ2", ...],
  "modality": "MRI",
  "dataset": "ISLES2022",
  "official_split": "train",
  "patient_id": "patient_id"
}

from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/isles2022")

# 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']}")

from huggingface_hub import snapshot_download
import nibabel as nib
import os

# Download the full dataset
local_path = snapshot_download(
    repo_id="Angelou0516/isles2022",
    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}")

@article{isles2022,
  title={ISLES 2022: Ischemic Stroke Lesion Segmentation Challenge},
  year={2023}
}