--- license: odc-by language: - en task_categories: - image-classification tags: - medical - brain-data - mri pretty_name: 3D Brain Structure MRI Scans --- ## 🧠 Dataset Summary 3794 anonymized 3D structural MRI brain scans (T1-weighted MPRAGE NIfTI files) from 2607 individuals included in five publicly available datasets: [DLBS](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html), [IXI](https://brain-development.org/ixi-dataset/), [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. 3529 scans come from cognitively normal individuals and 265 scans from individuals with an Alzheimer's disease clinical diagnosis. Scan image dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see methods). Scans have been processed and all protected health information (PHI) is excluded. Only the skull-stripped scan, integer age, biological sex, clinical diagnosis, and scan metadata are included. [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 include developing foundation-like models or tailored models for brain age prediction and disease classification. # License The use of the dataset as a whole is licensed under the ODC-By v1.0 license. Individual scans are licensed under study-specific data use agreements: IXI - [CC BY-SA 3.0](https://brain-development.org/ixi-dataset/) DLBS - [CC BY-NC 4.0](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html) NKI-RS - [Custom DUA](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing.html) OASIS-1 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) OASIS-2 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) The metadata provide the license for each object. # Sample images
18_F_CN_2966
Age 18 F, NKI-RS
Cognitively normal 		71_M_AD_3585
Age 71 M, OASIS-1
Alzheimer's disease 		46_F_CN_436
Age 46 F, IXI
Cognitively normal 		86_M_CN_3765
Age 86 M, OASIS-2
Cognitively normal
# Subject characteristics table | Split | n (scans) | n (subjects) | age_mean | age_std | age_range | sex_counts | diagnosis_counts | study_counts | |-------|-----------|--------------|-----------|-----------|-------------|--------------------------------|--------------------------|----------------------------------------------------------------------------| | train | 3066 | 2085 | 45.1 | 24.5 | (6, 98) | {'female': 1827, 'male': 1239} | {'CN': 2847, 'AD': 219} | {'NKI-RS': 1854, 'OASIS-1': 340, 'IXI': 326, 'OASIS-2': 296, 'DLBS': 250} | | validation | 364 | 261 | 46.4 | 24.5 | (6, 90) | {'female': 225, 'male': 139} | {'CN': 339, 'AD': 25} | {'NKI-RS': 213, 'IXI': 43, 'OASIS-1': 38, 'OASIS-2': 38, 'DLBS': 32} | | test | 364 | 261 | 45.7 | 24.6 | (6, 93) | {'female': 210, 'male': 154} | {'CN': 343, 'AD': 21} | {'NKI-RS': 216, 'IXI': 40, 'OASIS-2': 39, 'OASIS-1': 36, 'DLBS': 33} | # Folder organization ```bash brain-structure/ β”œβ”€ brain-structure.py β”œβ”€ metadata.csv β”œβ”€ IXI/ β”‚ β”œβ”€ sub-002/ β”‚ β”‚ └─ ses-01/ β”‚ β”‚ └─ anat/ β”‚ β”‚ β”œβ”€ msub-002_ses-01_T1w_brain_affine_mni.nii.gz β”‚ β”‚ └─ msub-002_ses-01_scandata.json β”‚ └─ ... β”œβ”€ DLBS/ β”‚ └─ ... └─ ... ``` # Example usage ``` # install Hugging Face Datasets pip install datasets # optional installs: NiBabel and PyTorch pip install nibabel pip install torch torchvision ``` ``` # load datasets from datasets import load_dataset ds_train = load_dataset("radiata-ai/brain-structure", split="train", trust_remote_code=True) ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True) ds_test = load_dataset("radiata-ai/brain-structure", split="test", trust_remote_code=True) ``` ``` # example PyTorch processing of images import nibabel as nib import torch import torch.nn.functional as F from torch.utils.data import Dataset def preprocess_nifti(example): """ Loads a .nii.gz file, crops, normalizes, and resamples to 96^3. Returns a numpy array (or tensor) in example["img"]. """ nii_path = example["nii_filepath"] # Load volume data vol = nib.load(nii_path).get_fdata() # Crop sub-volume vol = vol[7:105, 8:132, :108] # shape: (98, 124, 108) # Shift intensities to be non-negative vol = vol + abs(vol.min()) # Normalize to [0,1] vol = vol / vol.max() # Convert to torch.Tensor: (1,1,D,H,W) t_tensor = torch.from_numpy(vol).float().unsqueeze(0).unsqueeze(0) # Scale factor based on (124 -> 96) for the y-dimension scale_factor = 96 / 124 downsampled = F.interpolate( t_tensor, scale_factor=(scale_factor, scale_factor, scale_factor), mode="trilinear", align_corners=False ) # Now pad each dimension to exactly 96 (symmetric padding) _, _, d, h, w = downsampled.shape pad_d = 96 - d pad_h = 96 - h pad_w = 96 - w padding = ( pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2, pad_d // 2, pad_d - pad_d // 2 ) final_img = F.pad(downsampled, padding) # shape => (1, 1, 96, 96, 96) final_img = final_img.squeeze(0) # Store as numpy or keep as torch.Tensor example["img"] = final_img.numpy() return example ``` ``` # Apply the preprocessing to each split ds_train = ds_train.map(preprocess_nifti) ds_val = ds_val.map(preprocess_nifti) ds_test = ds_test.map(preprocess_nifti) # Set the dataset format to return PyTorch tensors for the 'img' column ds_train.set_format(type='torch', columns=['img']) ds_val.set_format(type='torch', columns=['img']) ds_test.set_format(type='torch', columns=['img']) # Set up data loaders for model training train_loader = DataLoader(ds_train, batch_size=16, shuffle=True) val_loader = DataLoader(ds_val, batch_size=16, shuffle=False) test_loader = DataLoader(ds_test, batch_size=16, shuffle=False) ``` # Study descriptions - IXI: A dataset of nearly 600 MR images from normal, healthy subjects, including T1, T2, PD-weighted, MRA, and diffusion-weighted images collected at three different hospitals in London. Citation: IXI data was obtained from https://brain-development.org/ixi-dataset/ - DLBS: A dataset from the Dallas Lifespan Brain Study (DLBS) comprising structural MRI, DTI, functional MRI, resting-state fMRI, and amyloid PET scans from 350 healthy adults aged 20-89, including extensive cognitive testing and demographic information. Citation: DLBS data was obtained from the International Neuroimaging Data-sharing Initiative (INDI) database. - NKI-RS: A large-scale ongoing neuroimaging dataset (N > 1000) across the lifespan from a community sample, including structural and functional MRI scans such as MPRAGE, DTI, resting-state fMRI, and task-based fMRI. Citation: NKI-RS data was obtained from Rockland Sample Neuroimaging Data Release. - OASIS-1: Cross-sectional T1-weighted MRI data from 416 right-handed subjects aged 18 to 96, including 100 over 60 with very mild to moderate Alzheimer’s disease, each with 3 or 4 scans. Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498 - OASIS-2: A longitudinal MRI dataset of 150 right-handed individuals aged 60-96, with 373 imaging sessions including T1-weighted MRIs, featuring nondemented and demented older adults, including patients with Alzheimer’s disease. 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)). 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'). 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. # Citation ``` @dataset{Radiata-Brain-Structure, author = {Jesse Brown and Clayton Young}, title = {Brain-Structure: Processed Structural MRI Brain Scans Across the Lifespan}, year = {2025}, url = {https://huggingface.co/datasets/radiata-ai/brain-structure}, note = {Version 1.0}, publisher = {Hugging Face} } ```