Abstract
Cerebral vein analysis provides a fundamental tool to study brain diseases such as neurodegenerative disorders or traumatic brain injuries. In order to assess the vascular anatomy, manual segmentation approaches can be used but are observer-dependent and time-consuming. In the present work, a fully automated cerebral vein segmentation method is proposed, based on a multiscale and multiparametric approach. The combined investigation of the R2(*)- and a Vesselness probability-map was used to obtain a fast and highly reliable classification of venous voxels. A semiquantitative analysis showed that our approach outperformed the previous state-of-the-art algorithm both in sensitivity and specificity. Inclusion of this tool within a parametric brain framework may therefore pave the way for a quantitative study of the intracranial venous system.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
