Visible Human Project: MRI of the head¶
pl.load("visible_human_head_mri") loads this dataset. Head MRI from the NLM Visible Human Project, as distributed with the SimpleITK notebooks. Small (2 MB), ideal for a first segmentation.
import pardesline as pl
ds = pl.load("visible_human_head_mri")
print(ds.describe()) # roles, frames, units, labels, licence, citation
s = ds[0]
obj = s.mri # fetched now, cached under ~/.cache/pardesline/datasets/visible_human_head_mri
Provenance and licence¶
| Source | direct HTTPS downloads with checksums: vm_head_mri.mha |
| Scheme | https |
| Size of the whole source | 2 MB |
| Licence | not a standard licence: see the note |
| Redistribution | unknown |
| Licence note | NLM Visible Human Project data; free since 2019 under NLM terms. Terms of use TO CONFIRM with the data owner before any redistribution. |
| Terms | https://www.nlm.nih.gov/research/visible/visible_human.html |
| Homepage | https://www.nlm.nih.gov/research/visible/visible_human.html |
| Cite | Ackerman M.J. The Visible Human Project. Proc. IEEE 86(3), 1998. |
| Samples grouped by | file |
| Samples listed in the manifest | 1 |
Roles (what each sample contains)¶
| Role | Type | Frame | Units | Labels / layout | Pattern or file |
|---|---|---|---|---|---|
mri |
volume (MR) | from the file header | from the file header | vm_head_mri.mhaHead MRI |
Standardized layout¶
Files are fetched on first access into the dataset cache and stay in their original format; ds.export(folder) writes every sample as standard files (PLY for points and meshes, MetaImage for volumes and label maps) with a .pardesline.json sidecar carrying frame, units, labels, licence and provenance, plus a manifest for the folder. Label maps become exact surfaces with to_mesh(label=k), DICOM series become one volume, raw arrays are decoded through the layout above.
ds = pl.load("visible_human_head_mri").take(2)
ds.export("./standardized") # PLY / MetaImage + sidecars + manifest.json
Algorithms and models that run on it¶
- volume roles:
volume.align(moving),volume.segment(lower, upper),volume.to_mesh(isovalue=...),volume.to_sitk(); catalog modules:pyvista_volume_processing,simpleitk_registration,simpleitk_segmentation - Self-supervised pretraining: 3D JEPA reads the volumes directly.
Related¶
- Datasets catalog · How to download a medical imaging dataset in Python · Add a dataset
- Tags:
medical,head,mri
Generated from src/pardesline/data/manifests/visible_human_head_mri.json on 2026-10-10.