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import os |
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import json |
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import logging |
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import datasets |
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logger = logging.getLogger(__name__) |
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_DESCRIPTION = """ |
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This dataset contains T1-weighted .nii.gz structural MRI scans in a BIDS-like arrangement. |
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Each scan has an associated JSON sidecar with metadata, including a 'split' field indicating |
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whether it's train, validation, or test. |
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""" |
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_CITATION = """ |
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@dataset{Radiata-Brain-Structure, |
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author = {Jesse Brown and Clayton Young}, |
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title = {Brain-Structure: Processed Structural MRI Brain Scans Across the Lifespan}, |
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year = {2025}, |
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url = {https://huggingface.co/datasets/radiata-ai/brain-structure}, |
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note = {Version 1.0}, |
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publisher = {Hugging Face} |
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} |
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""" |
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_HOMEPAGE = "https://huggingface.co/datasets/radiata-ai/brain-structure" |
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_LICENSE = "ODC-By v1.0" |
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class BrainStructureConfig(datasets.BuilderConfig): |
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""" |
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Configuration class for the Brain-Structure dataset. |
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""" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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class BrainStructure(datasets.GeneratorBasedBuilder): |
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""" |
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A dataset loader for T1 .nii.gz files plus JSON sidecars indicating splits |
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(train, validation, test). |
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Examples of how users typically load this dataset: |
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>>> from datasets import load_dataset |
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>>> ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True) |
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>>> ds_train = load_dataset("./brain-structure", split="train") # local clone |
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""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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BrainStructureConfig( |
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name="all", |
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version=VERSION, |
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description="All structural MRI data in a BIDS-like arrangement, labeled with train/val/test splits." |
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), |
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] |
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DEFAULT_CONFIG_NAME = "all" |
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def _info(self): |
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""" |
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Returns DatasetInfo, including the feature schema and other metadata. |
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""" |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"nii_filepath": datasets.Value("string"), |
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"metadata": { |
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"split": datasets.Value("string"), |
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"participant_id": datasets.Value("string"), |
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"session_id": datasets.Value("string"), |
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"study": datasets.Value("string"), |
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"age": datasets.Value("int32"), |
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"sex": datasets.Value("string"), |
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"clinical_diagnosis": datasets.Value("string"), |
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"scanner_manufacturer": datasets.Value("string"), |
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"scanner_model": datasets.Value("string"), |
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"field_strength": datasets.Value("string"), |
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"image_quality_rating": datasets.Value("float"), |
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"total_intracranial_volume": datasets.Value("float"), |
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"license": datasets.Value("string"), |
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"website": datasets.Value("string"), |
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"citation": datasets.Value("string"), |
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"t1_file_name": datasets.Value("string"), |
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"radiata_id": datasets.Value("int32"), |
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}, |
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} |
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), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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""" |
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Creates SplitGenerators for 'train', 'validation', and 'test'. |
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We locate the dataset files by referencing the directory of this script. |
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""" |
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data_dir = os.path.abspath(os.path.dirname(__file__)) |
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logger.info(f"BrainStructure: scanning data in {data_dir}") |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"data_dir": data_dir, "desired_split": "train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={"data_dir": data_dir, "desired_split": "validation"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"data_dir": data_dir, "desired_split": "test"} |
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), |
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] |
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def _generate_examples(self, data_dir, desired_split): |
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""" |
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Recursively walks the directory structure, looking for JSON sidecar files |
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ending in '_scandata.json'. For each matching file, yields an example |
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if sidecar["split"] == desired_split. |
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The corresponding .nii.gz is located by prefix matching. |
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""" |
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id_ = 0 |
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for root, dirs, files in os.walk(data_dir): |
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for fname in files: |
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if fname.endswith("_scandata.json"): |
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sidecar_path = os.path.join(root, fname) |
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with open(sidecar_path, "r") as f: |
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sidecar = json.load(f) |
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if sidecar.get("split") == desired_split: |
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nii_prefix = fname.replace("_scandata.json", "_T1w") |
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nii_filepath = None |
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for potential_file in files: |
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if potential_file.startswith(nii_prefix) and potential_file.endswith(".nii.gz"): |
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nii_filepath = os.path.join(root, potential_file) |
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break |
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if not nii_filepath: |
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logger.warning(f"No corresponding .nii.gz found for {sidecar_path}") |
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continue |
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yield id_, { |
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"id": str(id_), |
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"nii_filepath": nii_filepath, |
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"metadata": { |
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"split": sidecar.get("split", ""), |
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"participant_id": sidecar.get("participant_id", ""), |
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"session_id": sidecar.get("session_id", ""), |
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"study": sidecar.get("study", ""), |
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"age": sidecar.get("age", 0), |
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"sex": sidecar.get("sex", ""), |
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"clinical_diagnosis": sidecar.get("clinical_diagnosis", ""), |
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"scanner_manufacturer": sidecar.get("scanner_manufacturer", ""), |
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"scanner_model": sidecar.get("scanner_model", ""), |
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"field_strength": sidecar.get("field_strength", ""), |
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"image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)), |
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"total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)), |
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"license": sidecar.get("license", ""), |
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"website": sidecar.get("website", ""), |
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"citation": sidecar.get("citation", ""), |
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"t1_file_name": sidecar.get("t1_file_name", ""), |
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"radiata_id": sidecar.get("radiata_id", 0), |
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}, |
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} |
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id_ += 1 |
