Angelou0516/brats2023-ssa

BraTS 2023 Sub-Saharan Africa Dataset

Dataset Description

The BraTS 2023 Sub-Saharan Africa dataset for brain tumor segmentation (Sub-Saharan Africa cohort). This dataset contains MRI scans with dense segmentation annotations.

Dataset Details

• Modality: MRI
• Target: glioma regions
• Format: NIfTI (.nii.gz)

Dataset Structure

Each sample in the JSONL file contains:

{{
  "modalities": {{
    "t1n": "path/to/t1n.nii.gz",
    "t1c": "path/to/t1c.nii.gz",
    "t2w": "path/to/t2w.nii.gz",
    "t2f": "path/to/t2f.nii.gz"
  }},
  "mask": "path/to/seg.nii.gz",
  "label": ["necrotic brain tumor core", "brain edema", "enhancing brain tumor", "brain tumor"],
  "dataset": "{dataset_name}",
  "official_split": "train",
  "patient_id": "patient_id"
}}

Usage

Load Metadata

from datasets import load_dataset

# Load the dataset
ds = load_dataset("Angelou0516/brats2023-ssa")

# Access a sample
sample = ds['train'][0]
print(f"Patient ID: {sample['patient_id']}")
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/brats2023-ssa",
    repo_type="dataset"
)

# Load a sample
sample = ds['train'][0]
image = nib.load(os.path.join(local_path, sample["modalities"]["t1n"]))
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{brats2023_ssa,
  title={The BraTS 2023 Challenge on Glioma Segmentation in Sub-Saharan Africa},
  year={2023}
}

License

CC-BY-4.0

Dataset Homepage

https://www.synapse.org/#!Synapse:syn51156910