title: Automatic segmentation propagation of the aorta in real-time phase contrast MRI using nonrigid registration.
abstract: OBJECTIVE
  To assess the use of a nonrigid registration technique for semi-automatic segmentation of the aorta from real-time velocity mapping MRI.
  
  
  METHODS
  Real-time phase contrast images were acquired to measure flow and stroke volumes in 10 subjects, during free breathing, at rest, and during exercise. A nonrigid registration algorithm was developed to propagate a manually drawn region of interest in the aorta from one frame to all other frames of the real-time sequence (148 images). Thus the technique provided a semi-automatic segmentation over the whole sequence of images. The accuracy was assessed by comparison with manual segmentations in terms of Dice overlap measures and stroke volumes (SV).
  
  
  RESULTS
  Semi-automatic segmentations were comparable to manual ones (Dice score of 0.89 ± 0.04). Inter-observer reproducibility was similar for manual and semi-automatic segmentations (Dice score of 0.90 ± 0.04 in both cases, the difference was not significant). SV measurements also showed good agreement between manual and semi-automatic segmentations (correlation coefficient r > 0.94), and the differences were not statistically significant.
  
  
  CONCLUSIONS
  Although real-time phase contrast images have compromised image quality, a fast and robust segmentation of the aorta was possible using the registration-based technique.
authors.affiliation: Centre for Medical Image Computing, UCL Department of Medical Physics and Bioengineering, UK.
authors.forename: Freddy
authors.initials: F
authors.lastname: Odille
authors.forename: Jennifer A
authors.initials: JA
authors.lastname: Steeden
authors.forename: Vivek
authors.initials: V
authors.lastname: Muthurangu
authors.forename: David
authors.initials: D
authors.lastname: Atkinson
mesh_terms: D000328:Adult; D000465:Algorithms; D001011:Aorta / Q000033:anatomy & histology*; D001185:Artificial Intelligence; D003199:Computer Systems; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity