Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan

Deep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan

Abstract Background Peri-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...

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Bibliographic Details
Main Authors: Kilian Hett, Colin D. McKnight, Melanie Leguizamon, Jennifer S. Lindsey, Jarrod J. Eisma, Jason Elenberger, Adam J. Stark, Alexander K. Song, Megan Aumann, Ciaran M. Considine, Daniel O. Claassen, Manus J. Donahue
Format: Article
Language:English
Published: BMC 2024-02-01
Series:Fluids and Barriers of the CNS
Subjects:
Glymphatic
Parasagittal dural space
Arachnoid granulation
CSF flow
Machine learning
Online Access:https://doi.org/10.1186/s12987-024-00516-w

Internet

https://doi.org/10.1186/s12987-024-00516-w

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