Datasets:

pzarzycki
/

mri-oasis-1-ixi-pre

Error code:   StreamingRowsError
Exception:    ImportError
Message:      To support decoding NIfTI files, please install 'nibabel'.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2159, in _apply_feature_types_on_example
                  decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
                  column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/nifti.py", line 172, in decode_example
                  raise ImportError("To support decoding NIfTI files, please install 'nibabel'.")
              ImportError: To support decoding NIfTI files, please install 'nibabel'.

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OASIS-1 + IXI Preprocessed MRI

Preprocessed T1-weighted brain MRI volumes from two widely-used public neuroimaging datasets, ready for machine learning and 3-D visualisation.

Volumes are stored as float32 NumPy arrays of shape (96 × 128 × 96) (D × H × W), so loading is as simple as np.load("OAS1_0001_MR1_t1.npy").

Datasets

OASIS-1 — Open Access Series of Imaging Studies

Property	Value
Subjects	436 (ages 18–96)
Population	Cognitively normal adults + early-stage Alzheimer's disease
Clinical labels	CDR, MMSE, age, sex, education, SES, eTIV, nWBV
Channels per subject	3 (see below)

Channels:

Channel	Description
`t1`	Talairach-registered T1 (skull present, brain-masked approximation applied)
`t1_masked`	Skull-stripped T1 (FSL brain extraction)
`fseg`	FSL tissue segmentation: 1 = CSF, 2 = GM, 3 = WM

Clinical metadata is included as oasis_preprocessed/oasis_cross-sectional.csv with columns: ID, M/F, Hand, Age, Educ, SES, MMSE, CDR, eTIV, nWBV, ASF.

Citation:

Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of Cognitive Neuroscience, 19(9), 1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498

Original data: https://sites.wustl.edu/oasisbrains/

IXI — Information eXtraction from Images

Property	Value
Subjects	581 healthy volunteers
Sites	Hammersmith Hospital, Guy's Hospital, Institute of Psychiatry (London)
Channels per subject	1

Channels:

Channel	Description
`t1`	T1-weighted volume (skull-strip approximation + z-score normalised)

Citation:

IXI Dataset, Brain Development. Imperial College London. https://brain-development.org/ixi-dataset/

Original data: https://biomedic.doc.ic.ac.uk/brain-development/downloads/IXI/IXI-T1.tar

Preprocessing pipeline

All T1 channels follow the same pipeline:

Load with nibabel → squeeze singleton dimensions
Bounding-box crop (non-zero voxels + 8-voxel margin)
Approximate brain masking — zero out voxels below the 15th percentile of positive signal (applied to t1 channels that still contain skull)
Z-score normalise (computed on non-zero voxels only)
Transpose (x, y, z) → (z, y, x)
Trilinear resize to (96, 128, 96)

The fseg (segmentation) channel skips steps 2–4 and uses nearest-neighbour resize to preserve integer label values.

Preprocessing code: preprocess_oasis.py and download_ixi.py.

File structure

oasis_preprocessed/
├── oasis_cross-sectional.csv          # Clinical metadata (CDR, MMSE, age, …)
├── OAS1_0001_MR1_t1.npy               # shape (96, 128, 96), float32
├── OAS1_0001_MR1_t1_masked.npy
├── OAS1_0001_MR1_fseg.npy
└── … (436 subjects × 3 channels = 1 308 files, ~5.8 GB)

ixi_preprocessed/
├── IXI002-Guys-0828-T1.npy            # shape (96, 128, 96), float32
└── … (581 subjects × 1 channel = 581 files, ~2.6 GB)

Total: ~8.4 GB

Usage

import numpy as np
from huggingface_hub import hf_hub_download

# Download a single volume
path = hf_hub_download(
    repo_id="pzarzycki/mri-oasis-1-ixi-pre",
    repo_type="dataset",
    filename="oasis_preprocessed/OAS1_0001_MR1_t1.npy",
)
volume = np.load(path)   # (96, 128, 96) float32

Or with hf-mount for lazy access (no full download):

hf-mount start repo datasets/pzarzycki/mri-oasis-1-ixi-pre /data
ls /data/oasis_preprocessed/ | head
python -c "import numpy as np; print(np.load('/data/oasis_preprocessed/OAS1_0001_MR1_t1.npy').shape)"

Interactive viewer

This dataset powers the MRI Viewer space — a browser-based 3-D MRI viewer with patient browser, CDR/MMSE filtering, per-channel colormap/opacity controls, and WebGL2 volume rendering.

Source: github.com/pzarzycki/contrib-experiment — webapp/ directory.

Licence and attribution

This is a derivative dataset intended for non-commercial research and educational use only. Please respect the original licences:

OASIS-1: free for non-commercial research; cite Marcus et al. (2007); registration at oasisbrains.org required to access the raw data
IXI: Creative Commons CC BY-SA 3.0

Downloads last month: 1,750

Total file size:

54.1 GB