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import os
import json
import datasets
import logging
logger = logging.getLogger(__name__)
_DESCRIPTION = """
This dataset contains 3D MRI scans in NIfTI (.nii.gz) format, organized in a BIDS-like structure,
alongside JSON sidecar files with metadata. The data represents structural brain MRI scans from
multiple studies. For each scan, a `.nii.gz` file is provided, along with a `.json` file containing
subject/session metadata, scanner information, clinical diagnoses, etc.
"""
_CITATION = """
@article{exampleCitation2024,
title={Example Brain MRI Dataset Citation},
author={Your Name and Others},
journal={Journal of Great Datasets},
year={2024},
}
"""
_HOMEPAGE = "https://huggingface.co/datasets/radiata-ai/brains-structure"
_LICENSE = "Multiple study-specific licenses; see JSON sidecars for details."
class BrainsStructureConfig(datasets.BuilderConfig):
"""BuilderConfig for the Brains-Structure dataset."""
def __init__(self, **kwargs):
super(BrainsStructureConfig, self).__init__(**kwargs)
class BrainsStructure(datasets.GeneratorBasedBuilder):
"""
A Hugging Face dataset loader for the "Brains Structure" dataset
containing .nii.gz MRI scans plus JSON sidecar metadata.
Users must pass `trust_remote_code=True` to load, e.g.:
ds = load_dataset("radiata-ai/brains-structure", name="all", split="train", trust_remote_code=True)
"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
BrainsStructureConfig(
name="all",
version=VERSION,
description="All structural MRI data from multiple studies in a BIDS-like arrangement.",
),
]
DEFAULT_CONFIG_NAME = "all"
def _info(self):
"""
Return the dataset metadata: features, description, homepage, citation, etc.
"""
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"nii_filepath": datasets.Value("string"),
"metadata": {
"participant_id": datasets.Value("string"),
"session_id": datasets.Value("string"),
"study": datasets.Value("string"),
"age": datasets.Value("int32"),
"sex": datasets.Value("string"),
"clinical_diagnosis": datasets.Value("string"),
"scanner_manufacturer": datasets.Value("string"),
"scanner_model": datasets.Value("string"),
"field_strength": datasets.Value("string"),
"image_quality_rating": datasets.Value("string"),
"total_intracranial_volume": datasets.Value("string"),
"split": datasets.Value("string"),
"license": datasets.Value("string"),
"website": datasets.Value("string"),
"citation": datasets.Value("string"),
},
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
"""
If your data needs to be downloaded, you'd do that here.
However, we assume the data is already in the repository.
We'll discover the data locally.
"""
# We can get the data_dir from config/data_dir or from the repository local path
# By default, _split_generators receives a `dl_manager` that references the local dataset folder
data_dir = dl_manager.manual_dir if dl_manager.manual_dir else dl_manager.dataset_dir
# We'll just generate a single "train"/"validation"/"test" split
# based on the sidecar "split" field.
# Alternatively, you could create three separate splits if you want them enumerated automatically.
# For now, let's do a single "train" containing everything, or "all" approach.
# The simplest approach is to define them as separate splits:
# We'll parse the entire directory, grouping by sidecar "split" field.
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": data_dir, "split_key": "train"}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": data_dir, "split_key": "validation"}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_dir": data_dir, "split_key": "test"}),
]
def _generate_examples(self, data_dir, split_key):
"""
This function loads the .nii.gz + .json sidecar files from data_dir,
yields a tuple (id_, example).
The "split" field in the JSON sidecar indicates train/val/test.
We'll filter only those sidecars whose "split" matches `split_key`.
"""
# We'll do a recursive walk of data_dir. For each JSON sidecar, read it,
# check if "split" == split_key. If so, yield the example.
id_ = 0
for root, dirs, files in os.walk(data_dir):
# For each JSON file in the directory, attempt to pair it with the corresponding .nii.gz
for fname in files:
if fname.endswith("_scandata.json"):
sidecar_path = os.path.join(root, fname)
with open(sidecar_path, "r") as f:
metadata = json.load(f)
if metadata.get("split", None) == split_key:
# Build the .nii.gz path
# Typically, it's sub-XYZ_ses-XYZ_T1w.nii.gz or similar
# We'll guess it's in the same folder with a name that starts the same except `_scandata.json`
possible_nii_prefix = fname.replace("_scandata.json", "_T1w")
# Find any .nii.gz that starts with that prefix
nii_filepath = None
for possible_nii in files:
if possible_nii.startswith(possible_nii_prefix) and possible_nii.endswith(".nii.gz"):
nii_filepath = os.path.join(root, possible_nii)
break
# If not found, skip
if not nii_filepath:
logger.warning(f"No corresponding .nii.gz file found for {sidecar_path}")
continue
# Build the example
yield id_, {
"id": str(id_),
"nii_filepath": nii_filepath,
"metadata": {
"participant_id": str(metadata.get("participant_id", "")),
"session_id": str(metadata.get("session_id", "")),
"study": str(metadata.get("study", "")),
"age": metadata.get("age", None),
"sex": str(metadata.get("sex", "")),
"clinical_diagnosis": str(metadata.get("clinical_diagnosis", "")),
"scanner_manufacturer": str(metadata.get("scanner_manufacturer", "")),
"scanner_model": str(metadata.get("scanner_model", "")),
"field_strength": str(metadata.get("field_strength", "")),
"image_quality_rating": str(metadata.get("image_quality_rating", "")),
"total_intracranial_volume": str(metadata.get("total_intracranial_volume", "")),
"split": str(metadata.get("split", "")),
"license": str(metadata.get("license", "")),
"website": str(metadata.get("website", "")),
"citation": str(metadata.get("citation", "")),
},
}
id_ += 1
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