title: Automated contour detection in cardiac MRI using active appearance models: the effect of the composition of the training set.
abstract: OBJECTIVE
  Definition of the optimal training set for the automated segmentation of short-axis left ventricular magnetic resonance (MR) imaging studies in clinical practice based on active appearance model (AAM).
  
  
  METHODS
  We investigated the segmentation accuracy by varying the size and composition of the training set (ie, the ratio between pathologic and normal ventricle images, and the vendor dependence). The accuracy was assessed using the degree of similarity and the difference in ejection fraction between automatically detected and manually drawn contours.
  
  
  RESULTS
  Including more images in the training set results in a better accuracy of the detected contours, with optimum results achieved when including 180 images in the training set. Using AAM-based contour detection with a mixed model of 80% normal-20% pathologic images does provide good segmentation accuracy in clinical routine. Finally, it is essential to define different AAM models for different vendors of MRI systems.
  
  
  CONCLUSIONS
  A model defined on a sufficient number of images with the correct distribution of image characteristics achieves good matches in clinical routine. It is essential to define different AAM models for different vendors of MRI systems.
authors.affiliation: Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands. e.angelie@lumc.nl
authors.forename: Emmanuelle
authors.initials: E
authors.lastname: Angelié
authors.forename: Elco R
authors.initials: ER
authors.lastname: Oost
authors.forename: Dennis
authors.initials: D
authors.lastname: Hendriksen
authors.forename: Boudewijn P F
authors.initials: BP
authors.lastname: Lelieveldt
authors.forename: Rob J
authors.initials: RJ
authors.lastname: Van der Geest
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006352:Heart Ventricles / Q000473:pathology; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods; D008955:Models, Cardiovascular; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013318:Stroke Volume; D016276:Ventricular Function