title: Artificially enlarged training set in image segmentation.
abstract: Due to small training sets, statistical shape models constrain often too much the deformation in medical image segmentation. Hence, an artificial enlargement of the training set has been proposed as a solution for the problem. In this paper, the error sources in the statistical shape model based segmentation were analyzed and the optimization processes were improved. The method was evaluated with 3D cardiac MR volume data. The enlargement method based on non-rigid movement produced good results--with 250 artificial modes, the average error for four-chamber model was 2.11 mm when evaluated using 25 subjects.
authors.affiliation: Laboratory of Biomedical Engineering, Helsinki University of Technology, P.O.B. 2200, FIN-02015 HUT, Finland. tuomas.tolli@tkk.fi
authors.forename: Tuomas
authors.initials: T
authors.lastname: Tölli
authors.forename: Juha
authors.initials: J
authors.lastname: Koikkalainen
authors.forename: Kirsi
authors.initials: K
authors.lastname: Lauerma
authors.forename: Jyrki
authors.initials: J
authors.lastname: Lötjönen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006321:Heart / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity