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Related Experiment Video

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Development and validation of a perivascular space segmentation method in multi-center datasets.

Peiyu Huang1, Lingyun Liu1, Yao Zhang2

  • 1Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China; Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Neuroimage
|August 24, 2024
PubMed
Summary

A new neural network, mcPVS-Net, accurately quantifies perivascular spaces (PVS) in multi-center MRI scans. This tool shows promise for consistent PVS analysis across diverse neurological studies.

Keywords:
Deep LearningImage SegmentationMagnetic Resonance ImagingPerivascular spaces

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Area of Science:

  • Neuroimaging
  • Medical image analysis
  • Quantitative MRI

Background:

  • Perivascular spaces (PVS) on MRI are key indicators in neurological diseases.
  • Quantitative PVS analysis offers improved sensitivity and consistency.
  • A validated, multi-center method for PVS analysis is currently lacking.

Purpose of the Study:

  • To develop and validate a multi-center PVS segmentation network (mcPVS-Net).
  • To assess mcPVS-Net's accuracy and consistency across different MRI scanner vendors.
  • To explore the correlation between PVS volumes and clinical factors.

Main Methods:

  • mcPVS-Net was trained and tested on multi-center 3D T1-weighted (T1w) MRI data from Siemens, GE, and Philips scanners.
  • Segmentation accuracy was evaluated against ground truth masks and visual scores.
  • Correlations between PVS volumes and clinical factors (age, hypertension, WMH) were analyzed in 1020 subjects.
  • mcPVS-Net was also applied to T1w and T2w images from a United Imaging scanner.

Main Results:

  • mcPVS-Net achieved a mean DICE coefficient of 0.80, demonstrating good segmentation accuracy.
  • Segmentation performance was consistent across different scanner vendors.
  • PVS volumes significantly correlated with visual scores, age, hypertension, and white matter hyperintensities (WMH).
  • mcPVS-Net outperformed a previous method in PVS segmentation accuracy, especially in the basal ganglia.

Conclusions:

  • mcPVS-Net provides accurate PVS segmentation from 3D T1w MRI images.
  • The network demonstrates consistent performance across multiple scanner vendors.
  • mcPVS-Net is a promising tool for future multi-center PVS research.