OpenMind preprocessed: quasiraw-v2.1.1, cat12vbm-12.8.2, participants.tsv, val_split.csv

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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-4.0
+pretty_name: OpenMind (preprocessed, NeuroSpin)
+task_categories:
+- image-feature-extraction
+tags:
+- 3d
+- mri
+- neuroimaging
+- brain
+- medical
+- webdataset
+- self-supervised-learning
+- voxel-based-morphometry
+- cat12
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: quasiraw-v2.1.1
+  data_files:
+  - split: train
+    path: quasiraw-v2.1.1/*/*.tar
+- config_name: cat12vbm-12.8.2
+  data_files:
+  - split: train
+    path: cat12vbm-12.8.2/*/*.tar
+---
+
+# OpenMind: preprocessed brain MRI (NeuroSpin)
+
+Preprocessed versions of the [OpenMind dataset](https://huggingface.co/datasets/MIC-DKFZ/OpenMind)
+(Wald et al., 2025): head & neck 3D MRI pooled from ~800 [OpenNeuro](https://openneuro.org)
+datasets. Here the brain T1w, T2w and FLAIR images were preprocessed at
+[NeuroSpin](https://joliot.cea.fr/drf/joliot/en/Pages/research_entities/NeuroSpin.aspx)
+using [brainprep](https://github.com/brainprepdesk/brainprep) and packed as
+[WebDataset](https://github.com/webdataset/webdataset) shards, ready for large-scale
+(e.g. self-supervised) deep learning.
+
+| Folder | Pipeline | Status |
+|---|---|---|
+| `quasiraw-v2.1.1/` | brainprep quasi-raw v2.1.1 (T1w, T2w, FLAIR) | available |
+| `cat12vbm-12.8.2/` | CAT12 12.8.2 VBM via brainprep v2.1.0 (T1w): grey matter maps + ROI morphometry | available |
+
+All folders share the same sample identifiers: (`dataset_id`, `subject`, `session`, `run`),
+with `dataset_id` the shard folder name, so derivatives of the same scan can be joined
+across folders.
+
+## Repository layout
+
+```
+README.md
+val_split.csv                 # recommended validation split, for all folders (see below)
+participants.tsv              # per scan: age, sex, bmi, race, handedness, health_status
+quasiraw-v2.1.1/              # see below
+cat12vbm-12.8.2/              # see below
+```
+
+Each preprocessing folder holds one folder of WebDataset shards per OpenNeuro dataset and an
+`index.json.gz` with the byte offsets of every file of the shards, used by
+[nidl](#usage-with-nidl) for random access.
+
+## Validation split
+
+`val_split.csv` (columns `dataset_id, participant_id, session, run`) defines a held-out
+validation set made of **whole datasets** (no subject or dataset is shared with training),
+stratified on age, sex and BMI. It holds ~10% of the images. Only the rows listed in the 
+csv belong to validation. The others belong to training.
+
+The split is given by scan identifiers, shared by all folders: it applies to
+`quasiraw-v2.1.1` and `cat12vbm-12.8.2` alike.
+
+## Usage with nidl
+
+[nidl](https://github.com/neurospin-deepinsight/nidl) provides an `OpenMind`
+PyTorch `Dataset` that reads the shards by byte offset (from `index.json.gz`): random access,
+any sampler and any number of `DataLoader` workers, modality pairing and the subject-level
+train / val split. By default the images are streamed from this repository with HTTP range
+requests (nothing is written to disk but the index and the split); `streaming=False`
+downloads the needed shards first.
+
+```bash
+pip install "git+https://github.com/neurospin-deepinsight/nidl"
+```
+
+```python
+from torch.utils.data import DataLoader
+from nidl.datasets import OpenMind
+
+root = "openmind"  # local mirror of this repository, files are fetched on demand
+
+# Quasi-raw: one T1w image per item, subject-level split from <root>/val_split.csv
+train = OpenMind(root, preprocessing="quasiraw", modality="t1", split="train", apply_mask=True)
+val = OpenMind(root, preprocessing="quasiraw", modality="t1", split="val", apply_mask=True)
+print(train[0].shape)  # (1, 182, 218, 182)
+
+# Quasi-raw: paired T1w / T2w acquired in the same session
+paired = OpenMind(root, modality=("t1", "t2"), group_by="session", split="train")
+sample = paired[0]  # {"t1": array, "t2": array}
+
+# CAT12 VBM: grey matter map and ROI morphometry
+vbm = OpenMind(root, preprocessing="cat12vbm", split="train", return_morphometry=True)
+gm, morphometry = vbm[0]
+print(gm.shape, morphometry["total_volumes"]["TIV"])  # (1, 113, 137, 113) ...
+
+loader = DataLoader(train, batch_size=8, shuffle=True, num_workers=8)
+```
+
+`nidl` does not read the CAT12 quality ratings (`qc.json`): use the webdataset example of the
+`cat12vbm-12.8.2` section to get them.
+
+## `quasiraw-v2.1.1`
+
+### Content
+
+| | |
+|---|---|
+| Images | 71,490 (T1w 42,941 · T2w 23,935 · FLAIR 4,614) |
+| Subjects | 33,738 |
+| Source datasets | 787 OpenNeuro datasets |
+| Size | 462 GB, 849 shards |
+| Image grid | 182 × 218 × 182, 1 mm isotropic, MNI152 space, float32 |
+
+### Preprocessing
+
+[brainprep](https://github.com/brainprepdesk/brainprep) `subject-level-quasiraw` (v2.1.1
+container), applied per modality, with the matching MNI152 template (T1 template for T1w,
+T2 template for T2w / FLAIR):
+
+1. Reorientation to the MNI152 orientation.
+2. Brain mask with FreeSurfer [SynthStrip](https://surfer.nmr.mgh.harvard.edu/docs/synthstrip/).
+3. N4 bias field correction.
+4. Resampling to 1 mm isotropic.
+5. Linear (affine, 9 DOF) registration to the MNI152 1 mm template.
+6. The registration is applied to the bias-corrected image and the brain mask; the image
+   is then multiplied by the mask (skull-stripped).
+
+"Quasi-raw" means minimal preprocessing: no non-linear warp, no tissue segmentation, no
+intensity standardization across scans. Intensities are those of the bias-corrected scan.
+
+### Layout
+
+```
+quasiraw-v2.1.1/
+├── index.json.gz                     # byte offsets for random access (nidl)
+└── <dataset_id>/                     # one folder per OpenNeuro dataset, e.g. ds000001
+    └── <dataset_id>-NNNNNN.tar       # WebDataset shard(s), ≤ ~3 GB each
+```
+
+Each sample is one image of one (subject, session, run, modality). Its key is
+`sub-<label>_ses-<label>_mod-<t1|t2|flair>_run-<id>` (`ses-DEFAULT` when the source has no
+session) and it holds three files:
+
+| Extension | Content |
+|---|---|
+| `<mod>.nii.gz` | skull-stripped image in MNI space (`<mod>` = `t1`, `t2` or `flair`) |
+| `<mod>_mask.nii.gz` | binary brain mask, same grid |
+| `meta.json` | `subject`, `session`, `modality`, `run`, BIDS `entities` |
+
+Different subjects can have the same subject's id across different OpenNeuro datasets: a 
+subject is thus uniquely identified by the pair (`dataset_id`, `subject`), with `dataset_id` given 
+by the shard folder name.
+
+The `run` is the BIDS `run-` index of the source file when it is unambiguous, otherwise a
+hash of the source file name (brainprep convention).
+
+### Streaming with webdataset
+
+Without downloading (`pip install webdataset nibabel huggingface_hub`):
+
+```python
+import gzip, io, json
+import nibabel as nib
+import webdataset as wds
+from huggingface_hub import HfFileSystem, hf_hub_url
+
+repo = "neurospin/openmind"
+fs = HfFileSystem()
+shards = sorted(fs.glob(f"datasets/{repo}/quasiraw-v2.1.1/*/*.tar"))
+urls = [hf_hub_url(repo, s.split(f"{repo}/", 1)[1], repo_type="dataset") for s in shards]
+
+def load_nifti(b):
+    fh = nib.FileHolder(fileobj=io.BytesIO(gzip.decompress(b)))
+    return nib.Nifti1Image.from_file_map({"header": fh, "image": fh})
+
+def decode(s):
+    meta = json.loads(s["meta.json"])
+    mod = meta["modality"]
+    return {
+        "dataset_id": s["__url__"].rsplit("/", 2)[-2],
+        "meta": meta,
+        "image": load_nifti(s[f"{mod}.nii.gz"]).get_fdata(dtype="float32"),
+        "mask": load_nifti(s[f"{mod}_mask.nii.gz"]).get_fdata(dtype="float32") > 0,
+    }
+
+ds = wds.WebDataset([f"pipe:curl -s -L {u}" for u in urls], shardshuffle=True).map(decode)
+sample = next(iter(ds))
+print(sample["dataset_id"], sample["meta"], sample["image"].shape)
+```
+
+### Quality control
+
+No manual QC beyond the source curation was applied to this release: a small fraction of
+images may have failed skull-stripping or registration. The source OpenMind metadata
+includes a per-modality, per-dataset image quality score (1–5) that can be used to filter.
+
+## `cat12vbm-12.8.2`
+
+### Content
+
+| | |
+|---|---|
+| Images | 42,951 T1w (41,353 pass the CAT12 quality check, see below) |
+| Subjects | 32,995 |
+| Sessions | 38,047 |
+| Source datasets | 780 OpenNeuro datasets |
+| Size | 121 GB, 784 shards |
+| Image grid | 113 × 137 × 113, 1.5 mm isotropic, MNI152NLin2009cAsym space, float32 |
+| Morphometry | 15 atlases (ROI grey / white matter / CSF volumes) + total volumes |
+
+43,076 T1w images were processed: 125 are missing from this release because CAT12 failed
+on them or their group-level measures could not be computed.
+
+### Preprocessing
+
+[brainprep](https://github.com/brainprepdesk/brainprep) `subject-level-vbm` and
+`group-level-vbm` (v2.1.0 container) running
+[CAT12](https://neuro-jena.github.io/cat/) 12.8.2 (r2166, SPM12 standalone), with the
+brainprep CAT12 batch (cross-sectional, DARTEL registration):
+
+1. Bias correction, affine registration to MNI and tissue segmentation (grey matter, white
+   matter, CSF) with CAT12.
+2. Non-linear registration (DARTEL) to the CAT12 MNI152NLin2009cAsym template, resampled at
+   1.5 mm.
+3. The released image is the **modulated normalized grey matter map** (`mwp1`): GM tissue
+   probability multiplied by the Jacobian of the warp, so that voxel values sum to the
+   subject's GM volume.
+4. ROI volumes are computed by CAT12 in native space for each atlas; total intracranial
+   (TIV), grey matter, white matter and CSF volumes come from the CAT12 report.
+
+### Layout
+
+```
+cat12vbm-12.8.2/
+├── index.json.gz                     # byte offsets for random access (nidl)
+└── <dataset_id>/                     # one folder per OpenNeuro dataset, e.g. ds000001
+    └── <dataset_id>-NNNNNN.tar       # WebDataset shard(s), ≤ ~3 GB each
+```
+
+Each sample is one T1w image of one (subject, session, run). Its key is
+`sub-<label>_ses-<label>_mod-mwp1_run-<id>` and it holds four files:
+
+| Extension | Content |
+|---|---|
+| `mwp1.nii.gz` | modulated grey matter map in MNI space |
+| `morphometry.json` | `{<atlas>: {<feature>: value}}`, volumes in cm³ (see below) |
+| `qc.json` | `{"group_stats": {"NCR", "ICR", "IQR", "qc"}}` (see Quality control) |
+| `meta.json` | `dataset`, `subject`, `session`, `run` |
+
+`run` follows the same convention as `quasiraw-v2.1.1`: the BIDS `run-` index of the source
+file when it is unambiguous, otherwise a hash of the source file name (brainprep
+convention). A sample of this folder and the `quasiraw-v2.1.1` T1w sample of the same scan
+thus share (`dataset_id`, `subject`, `session`, `run`) in `meta.json`: 42,850 of the 42,951
+samples have such a quasi-raw counterpart (the other 101 scans failed quasi-raw
+preprocessing, mostly because their raw image contains NaN voxels).
+
+#### Morphometry
+
+`morphometry.json` holds one entry per atlas. Features are named `<tissue>_<ROI name>` with
+`<tissue>` in `Vgm` (grey matter), `Vwm` (white matter) and `Vcsf` (CSF) volumes, in cm³:
+
+| Atlas | Tissues | Features |
+|---|---|---|
+| `neuromorphometrics` | Vgm, Vwm, Vcsf | 408 |
+| `hammers` | Vgm, Vwm, Vcsf | 285 |
+| `ibsr` | Vgm, Vwm, Vcsf | 96 |
+| `lpba40` | Vgm, Vwm | 112 |
+| `cobra` | Vgm, Vwm | 104 |
+| `mori` | Vgm, Vwm | 256 |
+| `julichbrain` | Vgm, Vwm | 496 |
+| `suit` | Vgm, Vwm | 56 |
+| `aal3` | Vgm | 170 |
+| `thalamus` | Vgm | 14 |
+| `thalamic_nuclei` | Vgm | 22 |
+| `Schaefer2018_{100,200,400,600}Parcels_17Networks_order` | Vgm, Vwm | 200, 400, 800, 1200 |
+| `total_volumes` | | `TIV`, `GM_Vol`, `WM_Vol`, `CSF_Vol` |
+
+### Streaming with webdataset
+
+See [Usage with nidl](#usage-with-nidl) for random access. Streaming without downloading
+(`pip install webdataset nibabel huggingface_hub`):
+
+```python
+import gzip, io, json
+import nibabel as nib
+import webdataset as wds
+from huggingface_hub import HfFileSystem, hf_hub_url
+
+repo = "neurospin/openmind"
+fs = HfFileSystem()
+shards = sorted(fs.glob(f"datasets/{repo}/cat12vbm-12.8.2/*/*.tar"))
+urls = [hf_hub_url(repo, s.split(f"{repo}/", 1)[1], repo_type="dataset") for s in shards]
+
+def load_nifti(b):
+    fh = nib.FileHolder(fileobj=io.BytesIO(gzip.decompress(b)))
+    return nib.Nifti1Image.from_file_map({"header": fh, "image": fh})
+
+def decode(s):
+    return {
+        "dataset_id": s["__url__"].rsplit("/", 2)[-2],
+        "meta": json.loads(s["meta.json"]),
+        "gm": load_nifti(s["mwp1.nii.gz"]).get_fdata(dtype="float32"),
+        "morphometry": json.loads(s["morphometry.json"]),
+        "qc": json.loads(s["qc.json"])["group_stats"],
+    }
+
+ds = (wds.WebDataset([f"pipe:curl -s -L {u}" for u in urls], shardshuffle=True)
+      .map(decode)
+      .select(lambda s: s["qc"]["qc"] == 1))  # keep scans passing the CAT12 QC
+sample = next(iter(ds))
+print(sample["dataset_id"], sample["meta"], sample["gm"].shape,
+      sample["morphometry"]["total_volumes"]["TIV"])
+```
+
+### Quality control
+
+`qc.json` holds the CAT12 image quality ratings of the scan: noise to contrast ratio (`NCR`),
+inhomogeneity to contrast ratio (`ICR`) and the overall image quality rating (`IQR`), on the
+CAT12 grading scale (lower is better). `qc` is 1 when `NCR < 4.5` and `IQR < 4.5`, the
+thresholds of [OpenBHB](https://baobablab.github.io/bhb/) (Dufumier et al., 2022), and 0
+otherwise; 41,353 of the 42,951 images pass. No visual QC was applied.
+
+## Phenotypes / metadata
+
+`participants.tsv` (tab-separated) gives the phenotypes of every scan of this repository, one
+row per scan (66,661 rows, 33,827 subjects, 790 datasets), identified by `dataset_id`,
+`participant_id`, `session` and `run`: it joins with the `meta.json` of any folder on
+(`dataset_id`, `subject`, `session`, `run`). Missing values are `n/a`.
+
+| Column | Values | Available for |
+|---|---|---|
+| `age` | years (2–100) | 36,299 scans |
+| `sex` | `female`, `male` | 40,670 scans |
+| `bmi` | kg/m² | 6,107 scans |
+| `race` | e.g. `white/caucasian`, `african american/black`, `asian`, `multiracial/mixed` | 5,018 scans |
+| `handedness` | `right`, `left`, `ambidextrous` | 13,585 scans |
+| `health_status` | `healthy`, `ill` | 14,166 scans |
+
+Scanner information and the per-dataset image quality scores are in the original OpenMind
+release: see [MIC-DKFZ/OpenMind](https://huggingface.co/datasets/MIC-DKFZ/OpenMind).
+
+## License and citation
+
+The original OpenMind dataset is released under CC-BY 4.0 and is built from OpenNeuro
+datasets, most of which are released under CC0. This derivative is released under
+**CC-BY 4.0**. If you use it, please cite OpenMind, the OpenNeuro datasets you use,
+brainprep and, for `cat12vbm-12.8.2`, CAT12:
+
+```bibtex
+@article{wald2025openmind,
+  title   = {An OpenMind for 3D medical vision self-supervised learning},
+  author  = {Wald, Tassilo and others},
+  journal = {arXiv preprint arXiv:2412.17041},
+  year    = {2025}
+}
+
+@misc{brainprep,
+  title  = {{BrainPrep source code}},
+  author = {Grigis, Antoine and Victor, Julie and Dorval, Loic and Duchesnay, Edouard},
+  url    = {https://github.com/brainprepdesk/brainprep}
+}
+
+@article{gaser2024cat,
+  title   = {{CAT}: a computational anatomy toolbox for the analysis of structural {MRI} data},
+  author  = {Gaser, Christian and Dahnke, Robert and Thompson, Paul M. and Kurth, Florian
+             and Luders, Eileen and {the Alzheimer's Disease Neuroimaging Initiative}},
+  journal = {GigaScience},
+  volume  = {13},
+  year    = {2024}
+}
+```
+
+## Contact
+
+Benoit Dufumier, NeuroSpin (CEA, Université Paris-Saclay).

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