title: A semi-automated image segmentation approach for computational fluid dynamics studies of aortic dissection.
abstract: Computational studies of aortic hemodynamics require accurate and reproducible segmentation of the aortic tree from whole body, contrast enhanced CT images. Three methods were vetted for segmentation. A semi-automated approach that utilizes denoising, the extended maxima transform, and a minimal amount of manual segmentation was adopted.
authors.forename: Jeff R
authors.initials: JR
authors.lastname: Anderson
authors.forename: Christof
authors.initials: C
authors.lastname: Karmonik
authors.forename: Yannick
authors.initials: Y
authors.lastname: Georg
authors.forename: Jean
authors.initials: J
authors.lastname: Bismuth
authors.forename: Alan B
authors.initials: AB
authors.lastname: Lumsden
authors.forename: Adeline
authors.initials: A
authors.lastname: Schwein
authors.forename: Mickael
authors.initials: M
authors.lastname: Ohana
authors.forename: Fabien
authors.initials: F
authors.lastname: Thaveau
authors.forename: Nabil
authors.initials: N
authors.lastname: Chakfé
mesh_terms: D000465:Algorithms; D001011:Aorta / Q000473:pathology* / Q000503:physiopathology*; D001027:Aortography; D001331:Automation; D006801:Humans; D057446:Hydrodynamics; D007091:Image Processing, Computer-Assisted; D066330:Printing, Three-Dimensional; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed