ArticleFluids and barriers of the CNS2024
Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.
Article in Fluids and barriers of the CNS, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Beyond FLAIR: PRISM (polarity-preserving real inversion solute mapping)-a new Frontier in real inversion recovery MRI for intracranial and sensory organ fluid assessment.Japanese journal of radiology · 2026Review
- Imaging Biomarkers of the Glymphatic System in the Developing Brain.Korean journal of radiology · 2026Review
- Artificial intelligence-based segmentation of perisinusoidal tissue along the superior sagittal sinus in human brain magnetic resonance imaging.Neuroradiology · 2026Article
- Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease.Annals of clinical and translational neurology · 2026Article
- The parasagittal dural space of the human brain.Brain : a journal of neurology · 2025Review
- Parasagittal dural space and arachnoid granulations morphology in pre-clinical and early clinical multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Intravenous arachnoid granulation hypertrophy in patients with Parkinson disease.NPJ Parkinson's disease · 2024Article
- Diffusion tensor imaging along the perivascular space: the bias from crossing fibres.Brain communications · 2024Article
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12 authors.
Funding
Abstract
backgroundPeri-sinus structures such as arachnoid granulations (AG) and the parasagittal dural (PSD) space have gained much recent attention as sites of cerebral spinal fluid (CSF) egress and neuroimmune surveillance. Neurofluid circulation dysfunction may manifest as morphological changes in these structures, however, automated quantification of these structures is not possible and rather characterization often requires exogenous contrast agents and manual delineation.
methodsWe propose a deep learning architecture to automatically delineate the peri-sinus space (e.g., PSD and intravenous AG structures) using two cascaded 3D fully convolutional neural networks applied to submillimeter 3D T
resultsFindings demonstrate that the PSD and AG volumes can be segmented using T
conclusionsA tool that can be applied to quantify PSD and AG volumes from commonly acquired T
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Registered trials
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