ArticleIEEE transactions on bio-medical engineering2017
Structured Learning for 3-D Perivascular Space Segmentation Using Vascular Features.
Article in IEEE transactions on bio-medical engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 51 citations in OpenAlex.
- A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net.Neuroradiology · 2026Article
- From Vessels to Cognition: Glymphatic Alterations Following Hemorrhage in Cerebral Amyloid Angiopathy Are Associated With Small Vessel Disease Burden.Journal of the American Heart Association · 2025Article
- DRIPS: Domain Randomisation for Image-based Perivascular spaces Segmentation.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Beyond the cerebellum: perivascular space burden in spinocerebellar ataxia type 3 extends to multiple brain regions.Brain communications · 2025Article
- Overview of the Current Knowledge and Conventional MRI Characteristics of Peri- and Para-Vascular Spaces.Brain sciences · 2024Review
- Deep-learning-based segmentation of perivascular spaces on T2-Weighted 3T magnetic resonance images.Frontiers in aging neuroscience · 2024Article
- Quantitative assessment of enlarged perivascular spaces via deep-learning in community-based older adults reveals independent associations with vascular neuropathologies, vascular risk factors and cognition.Brain communications · 2024Article
- Perivascular network segmentations derived from high-field MRI and their implications for perivascular and parenchymal mass transport in the rat brain.Scientific reports · 2023Article
- Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter.Magnetic resonance in medicine · 2023Article
- Characteristics of perivascular space dilatation in normal aging.Human brain mapping · 2023Article
- Perivascular spaces relate to the course and cognition of Huntington's disease.Translational neurodegeneration · 2023Article
- Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.Neuroimage. Reports · 2023Article
- Automated Methods for Detecting and Quantitation of Enlarged Perivascular spaces on MRI.Journal of magnetic resonance imaging : JMRI · 2023Review
- Imaging perivascular space structure and function using brain MRI.NeuroImage · 2022Review
- Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework.Scientific reports · 2022Article
- A critical guide to the automated quantification of perivascular spaces in magnetic resonance imaging.Frontiers in neuroscience · 2022Review
- Semi-automated Segmentation and Quantification of Perivascular Spaces at 7 Tesla in COVID-19.Frontiers in neurology · 2022Article
- Body mass index, time of day and genetics affect perivascular spaces in the white matter.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2021Article
- 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network.Frontiers in neuroinformatics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
objectiveThe goal of this paper is to automatically segment perivascular spaces (PVSs) in brain from high-resolution 7T magnetic resonance (MR) images.
methodsWe propose a structured-learning-based segmentation framework to extract the PVSs from high-resolution 7T MR images. Specifically, we integrate three types of vascular filter responses into a structured random forest for classifying voxels into two categories, i.e., PVS and background. In addition, we propose a novel entropy-based sampling strategy to extract informative samples in the background for training an explicit classification model. Since the vascular filters can extract various vascular features, even thin and low-contrast structures can be effectively extracted from noisy backgrounds. Moreover, continuous and smooth segmentation results can be obtained by utilizing patch-based structured labels.
resultsThe performance of our proposed method is evaluated on 19 subjects with 7T MR images, with the Dice similarity coefficient reaching 66%.
conclusionThe joint use of entropy-based sampling strategy, vascular features, and structured learning can improve the segmentation accuracy. SIGNIFICANCE: Instead of manual annotation, our method provides an automatic way for PVS segmentation. Moreover, our method can be potentially used for other vascular structure segmentation because of its data-driven property.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.