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Related Experiment Video

Updated: May 1, 2026

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Perivascular space identification nnUNet for generalised usage (PINGU).

Benjamin Sinclair1, William Pham1, Lucy Vivash2

  • 1Department of Neuroscience, The School of Translational Medicine, Monash University, Melbourne, Australia.

Medical Image Analysis
|December 9, 2025
PubMed
Summary

A new deep learning model, PINGU, automates the segmentation of perivascular spaces (PVS) in brain MRIs. It shows strong performance, especially in the basal ganglia, offering a generalized solution for PVS analysis in diverse clinical settings.

Keywords:
Deep learningGlymphatic systemMRIPerivascular spacesUNet

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Area of Science:

  • Neuroimaging
  • Biomedical image analysis
  • Deep learning for medical applications

Background:

  • Perivascular spaces (PVS) are crucial for brain waste clearance via the glymphatic system.
  • Enlarged PVS on MRI are linked to aging and neurological diseases.
  • Manual PVS quantification is time-consuming and subjective, necessitating automated methods.

Purpose of the Study:

  • To develop and evaluate a generalized deep learning model for automated PVS segmentation across diverse MRI qualities and resolutions.
  • To compare the performance of the developed model against existing public deep learning algorithms for PVS segmentation.

Main Methods:

  • A nnUNet deep learning model was trained on a heterogeneous dataset of manually segmented MRIs from 7 datasets and 6 scanners.
  • The model, named PINGU (Perivascular space Identification Nnunet for Generalised Usage), was evaluated on various image qualities and resolutions.
  • PINGU's performance was compared against two publicly available 3D PVS segmentation algorithms.

Main Results:

  • PINGU achieved notable Dice scores in white matter (voxel: 0.50, cluster: 0.63) and basal ganglia (voxel: 0.54, cluster: 0.66).
  • While performance decreased on external datasets, PINGU still outperformed existing algorithms, particularly in the basal ganglia.
  • The model demonstrated superior generalization capabilities compared to current public PVS segmentation tools.

Conclusions:

  • PINGU offers a robust and generalized solution for automated PVS segmentation, addressing limitations of existing methods.
  • The model shows particular strength in segmenting PVS in the basal ganglia, a region relevant to vascular pathology.
  • PINGU has the potential to be a valuable tool for research and clinical applications involving PVS analysis.