title: Capturing large shape variations of liver using population-based statistical shape models.
abstract: OBJECTIVE
  Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures. We developed and tested a population-based model to represent the shape of liver.
  
  
  METHODS
  Upper abdominal CT-scan input images are represented by a conventional shape model. The shape parameters of individual livers extracted from the CT scans are employed to classify them into different populations. Corresponding to each population, an SSM model is built. The liver surface parameter space is divided into several subspaces which are more compact than the original space. The proposed model was tested using 29 CT-scan liver image data sets. The method was evaluated by model compactness, reconstruction error, generality and specificity measures.
  
  
  RESULTS
  The proposed model is implemented and tested using CT scans that included liver shapes with large shape variations. The method was compared with conventional and recently developed shape modeling methods. The accuracy of the proposed model was nearly twice that achieved with the conventional model. The proposed population-based model was more general compared with the conventional model. The mean reconstruction error of the proposed model was 0.029 mm while that of the conventional model was 0.052 mm.
  
  
  CONCLUSIONS
  A population-based model to represent the shape of liver was developed and tested with favorable results. Using this approach, the liver shapes from CT scans were modeled by a more compact, more general, and more accurate model.
authors.affiliation: Department of Biomedical Engineering, Engineering Faculty, Shahed University, Tehran, Iran. a.foruzan@ece.ut.ac.ir.|Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan. a.foruzan@ece.ut.ac.ir.
authors.forename: Amir H
authors.initials: AH
authors.lastname: Foruzan
authors.affiliation: Intelligent Image Processing Lab, College of Information Science and Engineering, Ritsumeikan University, Shiga, Japan.
authors.forename: Yen-Wei
authors.initials: YW
authors.lastname: Chen
authors.affiliation: Department of Radiology, Graduate School of Medicine, Osaka University, Osaka, Japan.
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Hori
authors.affiliation: Division of Image Analysis, Graduate School of Medicine, Osaka University, Osaka, Japan.
authors.forename: Yoshinobu
authors.initials: Y
authors.lastname: Sato
authors.affiliation: Department of Radiology, Graduate School of Medicine, Osaka University, Osaka, Japan.
authors.forename: Noriyuki
authors.initials: N
authors.lastname: Tomiyama
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional; D008099:Liver / Q000000981:diagnostic imaging*; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Medical image analysis; Population-based shape model; Shape representation; Statistical shape model
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