title: Computer-aided nidus segmentation and angiographic characterization of arteriovenous malformations.
abstract: OBJECTIVE
  Exact knowledge about the nidus of an arteriovenous malformation (AVM) and the connected vessels is often required for image-based research projects and optimal therapy planning. The aim of this work is to present and evaluate a computer-aided nidus segmentation technique and subsequent angiographic characterization of the connected vessels that can be visualized in 3D.
  
  
  METHODS
  The proposed method was developed and evaluated based on 15 datasets of patients with an AVM. Each dataset consists of a high-resolution 3D and a 4D magnetic resonance angiography (MRA) image sequence. After automatic cerebrovascular segmentation from the 3D MRA dataset, a voxel-wise support vector machine classification based on four extracted features is performed to generate a new parameter map. The nidus is represented by positive values in this parameter map and can be extracted using volume growing. Finally, the nidus segmentation is dilated and used for an automatic identification of feeding arteries and draining veins by integrating hemodynamic information from the 4D MRA datasets.
  
  
  RESULTS
  A quantitative comparison of the computer-aided AVM nidus segmentation results to manual gold-standard segmentations by two observers revealed a mean Dice coefficient of 0.835, which is comparable to the inter-observer agreement for which a mean Dice coefficient of 0.830 was determined. The angiographic characterization was visually rated feasible for all patients.
  
  
  CONCLUSIONS
  The presented computer-aided method enables a reproducible and fast extraction of the AVM nidus as well as an automatic angiographic characterization of the connected vessels, which can be used to support image-based research projects and therapy planning of AVMs.
authors.affiliation: Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Bldg. W36, Martinistraße 52, 20246 , Hamburg, Germany, n.forkert@uke.uni-hamburg.de.
authors.forename: Nils Daniel
authors.initials: ND
authors.lastname: Forkert
authors.forename: Till
authors.initials: T
authors.lastname: Illies
authors.forename: Einar
authors.initials: E
authors.lastname: Goebell
authors.forename: Jens
authors.initials: J
authors.lastname: Fiehler
authors.forename: Dennis
authors.initials: D
authors.lastname: Säring
authors.forename: Heinz
authors.initials: H
authors.lastname: Handels
mesh_terms: D002560:Cerebrovascular Circulation / Q000502:physiology*; D001330:Electronic Data Processing; D006801:Humans; D002538:Intracranial Arteriovenous Malformations / Q000175:diagnosis* / Q000503:physiopathology; D018810:Magnetic Resonance Angiography / Q000379:methods*; D015203:Reproducibility of Results