title: Elaboration of a semi-automated algorithm for brain arteriovenous malformation segmentation: initial results.
abstract: OBJECTIVE
  The purpose of our study was to distinguish the different components of a brain arteriovenous malformation (bAVM) on 3D rotational angiography (3D-RA) using a semi-automated segmentation algorithm.
  
  
  METHODS
  Data from 3D-RA of 15 patients (8 males, 7 females; 14 supratentorial bAVMs, 1 infratentorial) were used to test the algorithm. Segmentation was performed in two steps: (1) nidus segmentation from propagation (vertical then horizontal) of tagging on the reference slice (i.e., the slice on which the nidus had the biggest surface); (2) contiguity propagation (based on density and variance) from tagging of arteries and veins distant from the nidus. Segmentation quality was evaluated by comparison with six frame/s DSA by two independent reviewers. Analysis of supraselective microcatheterisation was performed to dispel discrepancy.
  
  
  RESULTS
  Mean duration for bAVM segmentation was 64 ± 26 min. Quality of segmentation was evaluated as good or fair in 93% of cases. Segmentation had better results than six frame/s DSA for the depiction of a focal ectasia on the main draining vein and for the evaluation of the venous drainage pattern.
  
  
  CONCLUSIONS
  This segmentation algorithm is a promising tool that may help improve the understanding of bAVM angio-architecture, especially the venous drainage.
  
  
  CONCLUSIONS
  • The segmentation algorithm allows for the distinction of the AVM's components • This algorithm helps to see the venous drainage of bAVMs more precisely • This algorithm may help to reduce the treatment-related complication rate.
authors.affiliation: Department of Interventional Neuroradiology, Pitié-Salpêtrière Hospital, Paris VI University, 47, Bd de l'Hôpital, 75013, Paris, France, fredclare5@msn.com.
authors.forename: Frédéric
authors.initials: F
authors.lastname: Clarençon
authors.forename: Franck
authors.initials: F
authors.lastname: Maizeroi-Eugène
authors.forename: Damien
authors.initials: D
authors.lastname: Bresson
authors.forename: Flavien
authors.initials: F
authors.lastname: Maingreaud
authors.forename: Nader
authors.initials: N
authors.lastname: Sourour
authors.forename: Claude
authors.initials: C
authors.lastname: Couquet
authors.forename: David
authors.initials: D
authors.lastname: Ayoub
authors.forename: Jacques
authors.initials: J
authors.lastname: Chiras
authors.forename: Catherine
authors.initials: C
authors.lastname: Yardin
authors.forename: Charbel
authors.initials: C
authors.lastname: Mounayer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D015901:Angiography, Digital Subtraction / Q000379:methods*; D001165:Arteriovenous Malformations / Q000000981:diagnostic imaging; D002533:Cerebral Angiography / Q000379:methods*; D002648:Child; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional; D002538:Intracranial Arteriovenous Malformations / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D055815:Young Adult