title: Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans.
abstract: OBJECTIVE
  CT angiography (CTA) scans are the current standard for vascular analysis of patients with cerebrovascular diseases, such as acute stroke and subarachnoid hemorrhage. An additional CT perfusion (CTP) scan is acquired of these patients to assess the perfusion of the cerebral tissue. The aim of this study is to extend the diagnostic yield of the CTP scans to also include vascular information.
  
  
  METHODS
  CTP scans are acquired by injecting contrast material and repeatedly scanning the head over time. Therefore, time-intensity profiles are available for each voxel in the scanned volume, resulting in a 4D dataset. These profiles can be utilized to differentiate not only between vessels and background but also between arteries and veins. In this article, a fully automatic method is proposed for the segmentation of the intracranial arteries and veins from 4D cerebral CTP scans. Furthermore, a vessel enhanced volume is presented, in which the vasculature is highlighted and background structures are suppressed. Combining this volume with the artery/vein segmentation results in an arteriogram and a venogram, which could serve as additional means for vascular analysis in patients with cerebrovascular diseases. The artery/vein segmentation is quantitatively evaluated by comparing the results to manual segmentations by two expert observers.
  
  
  RESULTS
  Results (paired two-tailed t-test) show that the accuracies of the proposed artery/vein labeling are not significantly different from the accuracies of the expert observer manual labeling (ground truth). Moreover, sensitivity and specificity of the proposed artery/vein labeling, relative to both expert observer ground truths, were similar to the sensitivity and specificity of the expert observer labeling compared to each other.
  
  
  CONCLUSIONS
  The proposed method for artery/vein segmentation is shown to be very accurate for arteries and veins with normal perfusion. Combining the artery/vein segmentation with the vessel enhanced volume produces an arteriogram and a venogram, which have the potential to extend the diagnostic yield of CTP scans and replace the additional CTA scan, but could also be helpful to radiologists in addition to the CTA scan.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. adrienne@isi.uu.nl
authors.forename: Adriënne
authors.initials: A
authors.lastname: Mendrik
authors.forename: Evert-Jan
authors.initials: EJ
authors.lastname: Vonken
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.forename: Ewoud
authors.initials: E
authors.lastname: Smit
authors.forename: Annet
authors.initials: A
authors.lastname: Waaije
authors.forename: Giovanna
authors.initials: G
authors.lastname: Bertolini
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
authors.forename: Mathias
authors.initials: M
authors.lastname: Prokop
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002533:Cerebral Angiography / Q000379:methods*; D002536:Cerebral Arteries / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D055420:Perfusion Imaging / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*