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README.md

@@ -1,7 +1,7 @@
 # Description
 3794 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [IXI](https://brain-development.org/ixi-dataset/), [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [NKI-RS](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing_neuro.html), [OASIS-1](https://sites.wustl.edu/oasisbrains/home/oasis-1/), and [OASIS-2](https://sites.wustl.edu/oasisbrains/home/oasis-2/). Subjects have a mean age of 45 ± 24. 3773 scans come from cognitively normal individuals and 261 scans from individuals with an Alzheimer's disease clinical diagnosis. Scans dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods).
 
-Scans are processed and no protected health information (PHI) is included - only the skull-stripped scan, integer age, biological sex, and clinical diagnosis. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create an open, unified, and harmonized dataset. This can be used for purposes including developing models for brain age prediction and disease classification. For more information see https://radiata.ai/public-studies.
+Scans are processed and no protected health information (PHI) is included - only the skull-stripped scan, integer age, biological sex, and clinical diagnosis. [Radiata](https://radiata.ai/) compiles and processes publicly available neuroimaging datasets to create this open, unified, and harmonized dataset. For more information see https://radiata.ai/public-studies. Example uses including developing foundation-like models or tailored models for brain age prediction and disease classification.
 
 
 # License
@@ -20,23 +20,23 @@ The metadata provide the license for each object.
 <table>
   <tr>
     <td align="center">
-      <img src="18_F_CN_2966.png" alt="18_F_CN_2966" width="150">
-      <br>Age 18 F
+      <img src="sample_images/18_F_CN_2966.png" alt="18_F_CN_2966" width="150">
+      <br>Age 18 F, NKI-RS
       <br>Cognitively normal
     </td>
     <td align="center">
-      <img src="71_M_AD_3585.png" alt="71_M_AD_3585" width="150">
-      <br>Age 71 M
+      <img src="sample_images/71_M_AD_3585.png" alt="71_M_AD_3585" width="150">
+      <br>Age 71 M, OASIS-1
       <br>Alzheimer's disease
     </td>
     <td align="center">
-      <img src="46_F_CN_436.png" alt="46_F_CN_436" width="150">
-      <br>Age 46 F
+      <img src="sample_images/46_F_CN_436.png" alt="46_F_CN_436" width="150">
+      <br>Age 46 F, IXI
       <br>Cognitively normal
     </td>
     <td align="center">
-      <img src="86_M_CN_3765.png" alt="86_M_CN_3765" width="150">
-      <br>Age 86 M
+      <img src="sample_images/86_M_CN_3765.png" alt="86_M_CN_3765" width="150">
+      <br>Age 86 M, OASIS-2
       <br>Cognitively normal
     </td>
   </tr>
@@ -53,15 +53,15 @@ The metadata provide the license for each object.
 
 # Folder organization
 ```bash
-brains-structure/
- ├─ brains-structure.py          
+brain-structure/
+ ├─ brain-structure.py          
  ├─ metadata.csv                
  ├─ IXI/
  │   ├─ sub-002/
  │   │   └─ ses-01/
  │   │       └─ anat/
- │   │           ├─ sub-002_ses-01_T1w_brain_affine_mni.nii.gz
- │   │           └─ sub-017_ses-01_scandata.json
+ │   │           ├─ msub-002_ses-01_T1w_brain_affine_mni.nii.gz
+ │   │           └─ msub-002_ses-01_scandata.json
  │   └─ ...
  ├─ DLBS/
  │   └─ ...
@@ -79,9 +79,9 @@ pip install datasets
 # load datasets
 from datasets import load_dataset
 
-ds_train = load_dataset("radiata-ai/brains-structure", name="all", split="train", trust_remote_code=True)
-ds_val   = load_dataset("radiata-ai/brains-structure", name="all", split="validation", trust_remote_code=True)
-ds_test  = load_dataset("radiata-ai/brains-structure", name="all", split="test", trust_remote_code=True)
+ds_train = load_dataset("radiata-ai/brain-structure", name="all", split="train", trust_remote_code=True)
+ds_val   = load_dataset("radiata-ai/brain-structure", name="all", split="validation", trust_remote_code=True)
+ds_test  = load_dataset("radiata-ai/brain-structure", name="all", split="test", trust_remote_code=True)
 ```
 
 ```
@@ -158,20 +158,27 @@ Citation: OASIS-2: Longitudinal: https://doi.org/10.1162/jocn.2009.21407
 
 # Methods
 ## Image processing 
-T1-weighted structural MRI scans were processed with [CAT12](https://neuro-jena.github.io/cat12-help/) ([Gaser et al, 2024](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520)). Voxel-based processing created tissue probability maps for gray matter/white matter/CSF. Scans were masked to brain-only and subsequently registered to ICBM 2009c Nonlinear Asymmetric space (MNI152NLin2009cAsym 1.5mm^3) using linear affine registration with 12 degrees of freedom in [FSL FLIRT](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/registration/flirt/index). The goal was to linearly align scans to standard space while preserving individual anatomy.
+T1-weighted structural MRI scans were processed with [CAT12](https://neuro-jena.github.io/cat12-help/) ([Gaser et al, 2024](https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giae049/7727520)). The image processing steps were:  
+- correct for bias, noise and intensity
+- mask to brain-only (gray matter + white matter + CSF)
+- register to ICBM 2009c Nonlinear Asymmetric space (MNI152NLin2009cAsym 1.5mm^3) using linear affine registration with 12 degrees of freedom in [FSL FLIRT](https://fsl.fmrib.ox.ac.uk/fsl/docs/#/registration/flirt/index) ('flirt -in t1.nii.gz -ref mni_icbm152_t1_tal_nlin_asym_09c_brain_1_5_mm.nii.gz -dof 12 -noresampblur').  
 
-Metadata includes the total intracranial volume (TIV), image quality rating (IQR), MRI scanner manufacturer/model, and field strength.
+The goal was to get denoised, unsmoothed scans that were maximally aligned to standard space while preserving individual anatomy.
+
+Metadata includes the total intracranial volume (TIV), image quality rating (IQR; larger value = worse quality), MRI scanner manufacturer/model, and field strength.
 
 ## Train/validation/test partitioning
-Scans were partitioned into train/validation/test datasets with a 80/10/10 split. Splits were balanced for age, sex, clinical diagnosis, and study. Subjects with multiple scans only appear in one split.
+Scans were partitioned into train/validation/test datasets with a 80%/10%/10% split. Splits were balanced for age, sex, clinical diagnosis, and study. Subjects with multiple scans only appear in one split.
 
 
 # Citation
-@dataset{Radiata-Brains-structure,
+```
+@dataset{Radiata-Brain-Structure,
   author    = {Jesse Brown and Clayton Young},
-  title     = {Brains-Structure: A Collection of Processed Structural MRI Scans},
+  title     = {Brain-Structure: A Collection of Processed Structural MRI Scans},
   year      = {2025},
-  url          = { https://huggingface.co/datasets/radiata-ai/brains-structure },
+  url          = { https://huggingface.co/datasets/radiata-ai/brain-structure },
   note      = {Version 1.0},
   publisher    = { Hugging Face }
-}
+}
+```

brain-structure.py

@@ -0,0 +1,185 @@
+import os
+import json
+import datasets
+import logging
+
+logger = logging.getLogger(__name__)
+
+_DESCRIPTION = """
+This dataset contains T1-weighted .nii.gz structural MRI scans in a BIDS-like arrangement.
+Each scan has an associated JSON sidecar with metadata, including fields such as subject
+demographics, scanner information, and a 'split' field indicating train/validation/test.
+"""
+
+_CITATION = """
+@dataset{Radiata-Brain-Structure,
+  author    = {Jesse Brown and Clayton Young},
+  title     = {Brain-Structure: A Collection of Processed Structural MRI Scans},
+  year      = {2025},
+  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
+  note      = {Version 1.0},
+  publisher = {Hugging Face}
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/radiata-ai/brain-structure"
+_LICENSE = "ODC-By v1.0"
+
+class BrainStructureConfig(datasets.BuilderConfig):
+    """
+    Configuration class for the Brain-Structure dataset.
+    You can define multiple configurations if needed (e.g. different subsets).
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+class BrainStructure(datasets.GeneratorBasedBuilder):
+    """
+    A dataset loader for T1 .nii.gz files plus JSON sidecars.
+    Each sidecar includes a 'split' field identifying whether the scan
+    belongs to the train, validation, or test set.
+
+    Usage Example:
+        ds = load_dataset(
+            "radiata-ai/brain-structure",
+            name="all",
+            split="train",
+            trust_remote_code=True
+        )
+    """
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        BrainStructureConfig(
+            name="all",
+            version=VERSION,
+            description=(
+                "All structural MRI data in a BIDS-like arrangement, labeled "
+                "with train/validation/test splits."
+            ),
+        ),
+    ]
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        """
+        Provides metadata about the dataset, including feature types
+        and general dataset information.
+        """
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "nii_filepath": datasets.Value("string"),
+                    "metadata": {
+                        "split": datasets.Value("string"),
+                        "participant_id": datasets.Value("string"),
+                        "session_id": datasets.Value("string"),
+                        "study": datasets.Value("string"),
+
+                        # Additional fields from the JSON sidecar
+                        "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("float"),
+                        "total_intracranial_volume": datasets.Value("float"),
+                        "license": datasets.Value("string"),
+                        "website": datasets.Value("string"),
+                        "citation": datasets.Value("string"),
+                        "t1_file_name": datasets.Value("string"),
+                        "radiata_id": datasets.Value("int32"),
+                    },
+                }
+            ),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        """
+        Returns SplitGenerators for 'train', 'validation', and 'test'.
+        Each split is identified by matching the 'split' field in the JSON sidecar.
+        """
+        data_dir = dl_manager.dataset_dir
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": data_dir, "desired_split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"data_dir": data_dir, "desired_split": "validation"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"data_dir": data_dir, "desired_split": "test"},
+            ),
+        ]
+
+    def _generate_examples(self, data_dir, desired_split):
+        """
+        Recursively scan the data_dir, locate JSON sidecar files, and yield
+        examples whose 'split' field matches desired_split.
+
+        Each yielded example includes:
+         - 'nii_filepath': pointing to the corresponding .nii.gz file
+         - 'metadata': dictionary of subject and scan information
+        """
+        id_ = 0
+        for root, dirs, files in os.walk(data_dir):
+            for fname in files:
+                if fname.endswith("_scandata.json"):
+                    sidecar_path = os.path.join(root, fname)
+                    with open(sidecar_path, "r") as f:
+                        sidecar = json.load(f)
+
+                    # Only yield if 'split' matches the desired split
+                    if sidecar.get("split") == desired_split:
+                        # Attempt to locate the matching .nii.gz file
+                        # Typically the sidecar is named sub-xxx_ses-xxx_scandata.json
+                        # and the NIfTI file: sub-xxx_ses-xxx_T1w.nii.gz
+                        possible_nii_prefix = fname.replace("_scandata.json", "_T1w")
+                        nii_filepath = None
+                        for potential_file in files:
+                            if (potential_file.startswith(possible_nii_prefix) 
+                                and potential_file.endswith(".nii.gz")):
+                                nii_filepath = os.path.join(root, potential_file)
+                                break
+
+                        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": {
+                                "split": sidecar.get("split", ""),
+                                "participant_id": sidecar.get("participant_id", ""),
+                                "session_id": sidecar.get("session_id", ""),
+                                "study": sidecar.get("study", ""),
+                                "age": sidecar.get("age", 0),  # default to 0 if missing
+                                "sex": sidecar.get("sex", ""),
+                                "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
+                                "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),
+                                "scanner_model": sidecar.get("scanner_model", ""),
+                                "field_strength": sidecar.get("field_strength", ""),
+                                "image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)),
+                                "total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)),
+                                "license": sidecar.get("license", ""),
+                                "website": sidecar.get("website", ""),
+                                "citation": sidecar.get("citation", ""),
+                                "t1_file_name": sidecar.get("t1_file_name", ""),
+                                "radiata_id": sidecar.get("radiata_id", 0),
+                            },
+                        }
+                        id_ += 1