title: A fully-automatic locally adaptive thresholding algorithm for blood vessel segmentation in 3D digital subtraction angiography.
abstract: Subarachnoid hemorrhage due to a ruptured cerebral aneurysm is still a devastating disease. Planning of endovascular aneurysm therapy is increasingly based on hemodynamic simulations necessitating reliable vessel segmentation and accurate assessment of vessel diameters. In this work, we propose a fully-automatic, locally adaptive, gradient-based thresholding algorithm. Our approach consists of two steps. First, we estimate the parameters of a global thresholding algorithm using an iterative process. Then, a locally adaptive version of the approach is applied using the estimated parameters. We evaluated both methods on 8 clinical 3D DSA cases. Additionally, we propose a way to select a reference segmentation based on 2D DSA measurements. For large vessels such as the internal carotid artery, our results show very high sensitivity (97.4%), precision (98.7%) and Dice-coefficient (98.0%) with our reference segmentation. Similar results (sensitivity: 95.7%, precision: 88.9% and Dice-coefficient: 90.7%) are achieved for smaller vessels of approximately 1mm diameter.
authors.forename: Marco
authors.initials: M
authors.lastname: Boegel
authors.forename: Philip
authors.initials: P
authors.lastname: Hoelter
authors.forename: Thomas
authors.initials: T
authors.lastname: Redel
authors.forename: Andreas
authors.initials: A
authors.lastname: Maier
authors.forename: Joachim
authors.initials: J
authors.lastname: Hornegger
authors.forename: Arnd
authors.initials: A
authors.lastname: Doerfler
mesh_terms: D000465:Algorithms; D015901:Angiography, Digital Subtraction / Q000379:methods*; D002343:Carotid Artery, Internal / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity