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A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net.

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  1. [1] § Methods › T1w+FLAIR nnU-Net ↔ scripts/pvs_predict_T1wFLAIR.sh, the whole file · a weak match · score 0.53 · FLAIR images, T1w images, co, Prediction, spacing, scans

Paper

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The authors' code

Shell · 19 lines · 825 B · MIT · 1 match

  1. #!/bin/bash
  3. # nnUNetv2 for the segmentation of perivascular spaces in T1w with co-registered FLAIR MRI scans
  4. # Author: William Pham
  5. # Date: 2025-11-04
  6. # Description: This script runs a nnUNet to label perivascular spaces and white matter hyperintensities in T1w and FLAIR MRI scans.
  7. # Usage:
  8. # Modify the script to include the INPUT_DIR and OUTPUT_DIR arguments, then run script:
  9. # ./pvs_predict_T1wFLAIR.sh
  11. # Input directory containing raw T1w and registered FLAIR Nifti images for inference
  12. # T1w images should end with "_0000.nii.gz"
  13. # FLAIR images should end with "_0001.nii.gz"
  14. INPUT_DIR=""
  16. # Output directory where PVS masks and model predictions will be outputs
  17. OUTPUT_DIR=""
  19. nnUNetv2_predict -d Dataset704_PVSFlair -i ${INPUT_DIR} -o ${OUTPUT_DIR} -f all -tr nnUNetTrainer -c 3d_fullres -p nnUNetResEncUNetMPlans

pvs_predict_T1wFLAIR.sh at commit dd7798d, under MIT · at the source

Overview

Authors: William Pham1, Alexander Jarema2, Donggyu Rim1, Zhibin Chen1, Mohamed Khlif1, Vaughan Macefield1,3, Luke Henderson4, Amy Brodtmann1
  1. Department of Neuroscience, Monash University,Melbourne, Australia
  2. Department of Radiology, Alfred Health,Melbourne, Australia
  3. School of Medicine, Western Sydney University,Sydney, Australia
  4. Department of Anatomy and Histology, The University of Sydney,Sydney, Australia
Institutions: Monash University (Australia); Alfred Health (Australia); Western Sydney University (Australia); The University of Sydney (Australia)
Journal: Neuroradiology, volume 68, issue 6, pages 1465-1483
Dates: received 8 October 2025; accepted 5 April 2026; published online 17 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s00234-026-03993-y · PMID 41995815 · PMCID PMC13323141 · OpenAlex W4405030596
Open access: hybrid, a free copy (OpenAlex)
Preprint: arxiv:2411.19564
Status: code verified
Categories: structural MRI / diffusion (modality), methods / tools (subfield)
Methods: Connectivity, Machine learning, Preprocessing, fMRI & imaging, Smoothing, state filtering, decompositions, Statistics
Keywords: Perivascular spaces, nnU-Net, Deep learning, MRI, Virchow-Robin spaces, Convolutional neural networks, 3T, 7T, Segmentation, U-Net
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Monash University
Citations: cited by 4 papers (Europe PMC); 60 references in the paper

Abstract

Background: Enlargement of perivascular spaces (PVS) is common in cerebral small vessel disease, Alzheimer’s disease, and Parkinson’s disease, reflecting impaired clearance pathways. While MRI provides a means to quantify perivascular spaces, manual annotation remains time-consuming and labour-intensive. Thus, there is a need for accurate automated MRI-based PVS segmentation methods.

Aim: To optimise the nnU-Net, a deep-learning framework, for PVS segmentation.

Methods: 30 T1-weighted (T1w) MRI images acquired on three different scanners were used. PVS in the white matter (WM) and basal ganglia (BG) were manually labelled via a sparse annotation strategy and used to optimise the nnU-Net for T1w PVS segmentation. The same pipeline was applied to T2-weighted (T2w) images. Additionally, we trained T1w+FLAIR and T2w+FLAIR models to simultaneously segment PVS and white matter hyperintensities (WMH). Performance was assessed with 5-fold cross validation using the Dice similarity coefficient (DSC).

Results: A voxel-spacing agnostic model (mean DSC = 64.3 ± 3.3%) outperformed models that resampled images to a common resolution (DSC = 40.5–55%). Training on PVS segmentations derived from preprocessed T1w images substantially improved performance (DSC = 78.3 ± 1.7%). The T2w model performed best overall (DSC = 84.7 ± 1.3%), especially for WM-PVS (DSC = 90.4 ± 0.9%) compared to BG-PVS (DSC = 79.1 ± 2%). Multimodal models achieved DSCs of 75.6 ± 3.4% (T1w+FLAIR) and 77.5 ± 2.7% (T2w+FLAIR).

Conclusions: Our deep learning models provide a robust framework for automated PVS quantification across MRI modalities.

Supplementary information: The online version contains supplementary material available at 10.1007/s00234-026-03993-y.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

wpham17/nnUNet-Perivascular-Spaces

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: dd7798d81617ec375450b7c350714c15f1e99639, 18 September 2026
Languages: Shell (8), Python (5)
Size: 17 files, 13 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: nnU-Net (12 files), NumPy (3 files), scikit-image (2 files), SimpleITK (2 files), DIPY (1 file), SciPy (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
15 files
At the source: github.com/wpham17/nnUNet-Perivascular-Spaces

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Our nnU-Net models are open-source and available at: [https://github.com/wpham17/nnUNet-Perivascular-Spaces](https://github.com/wpham17/nnUNet-Perivascular-Spaces).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 10 keywords, 1 funder, 57 references.

Cite

This paper

Pham, W., Jarema, A., Rim, D., Chen, Z., Khlif, M., Macefield, V., Henderson, L., & Brodtmann, A. (2026). A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net. Neuroradiology, 68(6), 1465-1483. https://doi.org/10.1007/s00234-026-03993-y

BibTeX

@article{pham2026comprehensive,
author = {Pham, William and Jarema, Alexander and Rim, Donggyu and Chen, Zhibin and Khlif, Mohamed and Macefield, Vaughan and Henderson, Luke and Brodtmann, Amy},
title = {{A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net}},
journal = {Neuroradiology},
year = {2026},
month = apr,
volume = {68},
number = {6},
pages = {1465--1483},
publisher = {Springer Science+Business Media},
issn = {0028-3940},
doi = {10.1007/s00234-026-03993-y},
url = {https://doi.org/10.1007/s00234-026-03993-y},
pmid = {41995815},
pmcid = {PMC13323141}
}

RIS

TY - JOUR
AU - Pham, William
AU - Jarema, Alexander
AU - Rim, Donggyu
AU - Chen, Zhibin
AU - Khlif, Mohamed
AU - Macefield, Vaughan
AU - Henderson, Luke
AU - Brodtmann, Amy
TI - A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net
T2 - Neuroradiology
J2 - Neuroradiology
PY - 2026
DA - 2026/04/17
VL - 68
IS - 6
SP - 1465
EP - 1483
SN - 0028-3940
PB - Springer Science+Business Media
DO - 10.1007/s00234-026-03993-y
UR - https://doi.org/10.1007/s00234-026-03993-y
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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