title: A multiparametric and multiscale approach to automated segmentation of brain veins.
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.
authors.forename: S
authors.initials: S
authors.lastname: Monti
authors.forename: G
authors.initials: G
authors.lastname: Palma
authors.forename: P
authors.initials: P
authors.lastname: Borrelli
authors.forename: E
authors.initials: E
authors.lastname: Tedeschi
authors.forename: S
authors.initials: S
authors.lastname: Cocozza
authors.forename: M
authors.initials: M
authors.lastname: Salvatore
authors.forename: M
authors.initials: M
authors.lastname: Mancini
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply*; D002550:Cerebral Veins; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results