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MRI Brain

HCP-Aging perivascular space labels

Precise perivascular space segmentation on T2-weighted magnetic resonance imaging from Human Connectome Project-Aging

Brain T2-weighted MRI of healthy adults aged 36 to 100 with refined masks of white matter perivascular spaces, made to train and test automatic segmentation of these small fluid-filled channels. 200 people selected from HCP-Aging for an even spread of age and sex.

Overview

This release pairs T2-weighted brain scans from 200 HCP-Aging participants with voxel masks of perivascular spaces (PVS) in the white matter. PVS are fluid-filled channels around small vessels; when enlarged they are a marker of cerebral small vessel disease. They are thin and hard to label by hand. The authors built the masks to give segmentation methods a reference set across the whole adult age range. The images are a subset of HCP-Aging (Lifespan 2.0) and are shared again here under CC0 on OpenNeuro, so they can be used without the NIMH Data Archive agreement that governs the full HCP-Aging release.

Composition

From 765 HCP-Aging subjects with imaging, the authors picked 217 to get an even spread of age and sex and dropped 17 after visual checks (11 for motion, 6 for very large lacunar infarcts). The remaining 200 are healthy adults aged 36 to 100, 113 women and 87 men, with roughly 32 to 34 people per decade from the thirties to the eighties and 4 aged 90 or older. Each subject has one T2w volume and one binary PVS mask. The participants file holds only age and sex.

Acquisition

Scans come from Siemens 3T Prisma scanners with a 32-channel head coil. The T2w scan is a turbo spin echo at 0.8 mm isotropic resolution (TR 3200 ms, TE 564 ms). Images are de-identified NIfTI files in BIDS layout.

Annotations

Labels were built in stages. A Frangi vesselness filter inside a white matter mask gave initial candidates. Two experts corrected these by hand in ITK-SNAP for 40 subjects, four to five per decade of life. A semi-supervised U-Net trained on those 40 then predicted PVS in the other 160, and two neuroradiologists and a trained medical student corrected every prediction in weekly consensus meetings. On 10 cases rated by three raters the paper reports a Fleiss kappa of 0.95 and pairwise Dice of 0.94 to 0.97. PVS in deep grey matter are not labeled.

Known limitations

  • White matter PVS only; basal ganglia and other deep grey matter PVS are left out.
  • Only T2w images are shared; T1w and other HCP-Aging contrasts stay behind the NDA agreement.
  • The paper is a preprint and states the age range inconsistently (30 to 100 in places); the participants file gives 36 to 100.

Cohort

Aggregate numbers from the sources below. Bars are relative to the 200 subjects.

Contrast / sequence

Groups can overlap

  • T2-weighted 200 100%

sub-*/anat/*_T2w.nii.gz in BIDS file tree of OpenNeuro ds005595 (CC0), T2w images and PVS masks counted per subject

Scanner vendor

  • Siemens Healthineers 200 100%

Methods: Imaging parameters in Chai et al. 2025, Precise perivascular space segmentation on MRI from HCP-Aging, medRxiv preprint (PMC11957161)

Age

mean 61.1, median 60.5, range 36 to 100

0
10
20
30
34
30-3940-4950-5960-6970-7980-8990+

age in participants.tsv of OpenNeuro ds005595 (CC0), counted per row; sex from sex, ages from age

Condition

Groups can overlap

  • Healthy control 200 100%

Methods: Subjects in Chai et al. 2025, Precise perivascular space segmentation on MRI from HCP-Aging, medRxiv preprint (PMC11957161)

License and access

Our reading of the license, not legal advice. Before you use the data, read the original license and confirm that your use is allowed. We take no responsibility for how you use a dataset. Full disclaimer

Access
Open download

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Access page

Creative Commons Zero 1.0 Universal

Public domain dedication. Do anything with the data, including commercial use, without asking and without having to give credit.

License stated in dataset_description.json of the OpenNeuro release. The source images also remain available through HCP-Aging on the NIMH Data Archive under that study's own data use terms.

Original license text Version read: 1.0 Checked 2026-10-07

What you can do

  • Yes
  • Yes
  • Yes
  • Yes
  • Yes

What you can share

  • Yes
  • Yes
  • Yes

What you must do

  • No
  • Share alike No
  • No
  • No
  • Manuscript review No
  • Release code No
  • Return results No
  • Delete after use No

Limits

  • No
  • Location limits No

Citation

Chai Y, Zhang H, Robles C, Kim AS, et al. Precise perivascular space segmentation on magnetic resonance imaging from Human Connectome Project-Aging. medRxiv (2025). doi:10.1101/2025.03.19.25324269

Sources

Every number on this page comes from one of these documents. Each chart names the table or page it is taken from. The raw numbers are in stats.csv.