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--- |
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license: odc-by |
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language: |
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- en |
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task_categories: |
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- image-classification |
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tags: |
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- medical |
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- brain-data |
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- mri |
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pretty_name: 3D Brain Structure MRI Scans |
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--- |
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## 🧠 Dataset Summary |
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3794 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. 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). |
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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 including developing foundation-like models or tailored models for brain age prediction and disease classification. |
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# License |
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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: |
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IXI - [CC BY-SA 3.0](https://brain-development.org/ixi-dataset/) |
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DLBS - [CC BY-NC 4.0](https://fcon_1000.projects.nitrc.org/indi/retro/dlbs.html) |
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NKI-RS - [Custom DUA](https://fcon_1000.projects.nitrc.org/indi/enhanced/sharing.html) |
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OASIS-1 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) |
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OASIS-2 - [Custom DUA](https://sites.wustl.edu/oasisbrains/) |
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The metadata provide the license for each object. |
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# Sample images |
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<table> |
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<tr> |
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<td align="center"> |
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<img src="sample_images/18_F_CN_2966.png" alt="18_F_CN_2966" width="150"> |
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<br>Age 18 F, NKI-RS |
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<br>Cognitively normal |
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</td> |
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<td align="center"> |
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<img src="sample_images/71_M_AD_3585.png" alt="71_M_AD_3585" width="150"> |
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<br>Age 71 M, OASIS-1 |
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<br>Alzheimer's disease |
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</td> |
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<td align="center"> |
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<img src="sample_images/46_F_CN_436.png" alt="46_F_CN_436" width="150"> |
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<br>Age 46 F, IXI |
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<br>Cognitively normal |
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</td> |
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<td align="center"> |
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<img src="sample_images/86_M_CN_3765.png" alt="86_M_CN_3765" width="150"> |
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<br>Age 86 M, OASIS-2 |
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<br>Cognitively normal |
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</td> |
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</tr> |
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</table> |
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# Subject characteristics table |
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| Split | n (scans) | n (subjects) | age_mean | age_std | age_range | sex_counts | diagnosis_counts | study_counts | |
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|-------|-----------|--------------|-----------|-----------|-------------|--------------------------------|--------------------------|----------------------------------------------------------------------------| |
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| 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} | |
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| 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} | |
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| 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} | |
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# Folder organization |
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```bash |
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brain-structure/ |
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├─ brain-structure.py |
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├─ metadata.csv |
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├─ IXI/ |
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│ ├─ sub-002/ |
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│ │ └─ ses-01/ |
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│ │ └─ anat/ |
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│ │ ├─ msub-002_ses-01_T1w_brain_affine_mni.nii.gz |
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│ │ └─ msub-002_ses-01_scandata.json |
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│ └─ ... |
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├─ DLBS/ |
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│ └─ ... |
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└─ ... |
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``` |
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# Example usage |
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``` |
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# install Hugging Face Datasets library |
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pip install datasets |
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``` |
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``` |
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# load datasets |
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from datasets import load_dataset |
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ds_train = load_dataset("radiata-ai/brain-structure", name="all", split="train", trust_remote_code=True) |
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ds_val = load_dataset("radiata-ai/brain-structure", name="all", split="validation", trust_remote_code=True) |
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ds_test = load_dataset("radiata-ai/brain-structure", name="all", split="test", trust_remote_code=True) |
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``` |
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``` |
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# example PyTorch processing of images |
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import torch |
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import torch.nn.functional as F |
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from torch.utils.data import Dataset |
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import nibabel as nib |
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class NiiDataset(Dataset): |
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""" |
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A PyTorch Dataset that wraps a Hugging Face Dataset containing |
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MRI .nii.gz file paths, and loads/normalizes/resamples each volume. |
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""" |
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def __init__(self, hf_dataset): |
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""" |
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hf_dataset: a Hugging Face Dataset object, e.g. ds_train |
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(each example should have a 'nii_filepath') |
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""" |
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self.hf_dataset = hf_dataset |
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def __len__(self): |
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return len(self.hf_dataset) |
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def __getitem__(self, idx): |
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# Load the dataset example |
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example = self.hf_dataset[idx] |
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nii_path = example["nii_filepath"] # e.g., "IXI/sub-017/ses-01/anat/sub-017_ses-01_T1w.nii.gz" |
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# Load the .nii.gz file with nibabel |
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cur_t1_file = nib.load(nii_path) |
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t1_data = cur_t1_file.get_fdata() |
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# Preprocess: example sub-volume |
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# (7:105, 8:132, :108) => shape: (98, 124, 108) |
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t1_data = t1_data[7:105, 8:132, :108] |
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# Normalize intensities |
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t1_data = t1_data / t1_data.max() |
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# Convert to PyTorch tensor, add two channel dims for volumetric interpolation |
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# shape => (1, 1, 98, 124, 108) |
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t1_tensor = torch.from_numpy(t1_data).float().unsqueeze(0).unsqueeze(0) |
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# Downsample/resample to e.g. (96, 96, 96) |
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img_downsample = F.interpolate( |
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t1_tensor, |
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size=(96, 96, 96), |
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mode="trilinear", |
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align_corners=False |
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) |
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# shape => (1, 96, 96, 96) |
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# Squeeze out the channel dim if needed: shape => (96, 96, 96) |
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img_downsample = img_downsample.squeeze(0) |
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sample = {"img": img_downsample} |
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return sample |
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``` |
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# Study descriptions |
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- 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. |
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Citation: IXI data was obtained from https://brain-development.org/ixi-dataset/ |
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- 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. |
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Citation: DLBS data was obtained from the International Neuroimaging Data-sharing Initiative (INDI) database. |
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- 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. |
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Citation: NKI-RS data was obtained from Rockland Sample Neuroimaging Data Release. |
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- 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. |
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Citation: OASIS-1: Cross-Sectional: https://doi.org/10.1162/jocn.2007.19.9.1498 |
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- 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. |
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Citation: OASIS-2: Longitudinal: https://doi.org/10.1162/jocn.2009.21407 |
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# Methods |
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## Image processing |
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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: |
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- correct for bias, noise, and intensity |
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- mask to brain-only (gray matter + white matter + CSF) |
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- 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'). |
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The goal was to get denoised, unsmoothed scans that were maximally aligned to standard space while preserving individual anatomy. |
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Metadata includes the total intracranial volume (TIV), image quality rating (IQR; larger value = worse quality), MRI scanner manufacturer/model, and field strength. |
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## Train/validation/test partitioning |
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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. |
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# Citation |
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``` |
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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: A Collection of Processed Structural MRI Scans}, |
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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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``` |
