ReviewFrontiers in neuroscience2022
A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.
Review in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Tracking the changes in longitudinal MRI-detected perivascular spaces following ischaemic stroke.European stroke journal · 2026Article
- Tubule and microbranch variations in human dentine: A quantitative 3D study with serial block-face scanning electron microscopy.Journal of microscopy · 2026Article
- Glymphatic Dysfunction and Aquaporin-4 Dysregulation in Traumatic Brain Injury and Brain Tumors: A Review.Molecular neurobiology · 2026Review
- Enlargement of perivascular spaces associated with habitual sake (Japanese rice wine) consumption in participants of brain health checkups.Neuroradiology · 2026Article
- A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net.Neuroradiology · 2026Article
- Genetic Architecture of Perivascular Space Morphology in the Pediatric Brain.bioRxiv : the preprint server for biology · 2026Article
- Improved visualization of perivascular spaces on T2-weighted imaging with deep learning-based denoising and super-resolution reconstruction.Scientific reports · 2026Article
- The Prevalence of Sleep Disorders in Populations with Glymphatic Dysfunction: A Systematic Review and Meta-Analysis.Biology · 2026Review
- segcsvdHuman brain mapping · 2026Article
- The enigma of vascular dementia: current state and emerging perspectives.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults.Scientific reports · 2026Article
- "Mind the Gap"-enlarged perivascular spaces as a potential magnetic resonance imaging biomarker of impaired glymphatic clearance in brain disorders.Frontiers in cellular neuroscience · 2026Review
- Cytopathological Findings of Axillary Lymph Nodes in Patients with Breast Cancer in Correlation with Ultrasound Findings.Iranian journal of pathology · 2026Article
- DRIPS: Domain Randomisation for Image-based Perivascular spaces Segmentation.medRxiv : the preprint server for health sciences · 2025Article
- Imaging brain fluid dynamics and waste clearance involving perivascular spaces in cerebral small vessel disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Cerebral small vessel disease lesion segmentation methods: A systematic review.Cerebral circulation - cognition and behavior · 2025Review
- MRI quantified enlarged perivascular space volumes as imaging biomarkers correlating with severity of anxiety depression in young adults with long-time mobile phone use.Frontiers in psychiatry · 2025Article
- Understanding central nervous system fluid networks: Historical perspectives and a revised model for clinical neurofluid imaging.NMR in biomedicine · 2024Review
- A "glympse" into neurodegeneration: Diffusion MRI and cerebrospinal fluid aquaporin-4 for the assessment of glymphatic system in Alzheimer's disease and other dementias.Human brain mapping · 2024Article
- Time-of-Flight MRA of Intracranial Aneurysms with Interval Surveillance, Clinical Segmentation and Annotations.Scientific data · 2024Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The glymphatic system is responsible for waste clearance in the brain. It is comprised of perivascular spaces (PVS) that surround penetrating blood vessels. These spaces are filled with cerebrospinal fluid and interstitial fluid, and can be seen with magnetic resonance imaging. Various algorithms have been developed to automatically label these spaces in MRI. This has enabled volumetric and morphological analyses of PVS in healthy and disease cohorts. However, there remain inconsistencies between PVS measures reported by different methods of automated segmentation. The present review emphasizes that importance of voxel-wise evaluation of model performance, mainly with the Sørensen Dice similarity coefficient. Conventional count correlations for model validation are inadequate if the goal is to assess volumetric or morphological measures of PVS. The downside of voxel-wise evaluation is that it requires manual segmentations that require large amounts of time to produce. One possible solution is to derive these semi-automatically. Additionally, recommendations are made to facilitate rigorous development and validation of automated PVS segmentation models. In the application of automated PVS segmentation tools, publication of image quality metrics, such as the contrast-to-noise ratio, alongside descriptive statistics of PVS volumes and counts will facilitate comparability between studies. Lastly, a head-to-head comparison between two algorithms, applied to two cohorts of astronauts reveals how results can differ substantially between techniques.
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