title: The use of an active appearance model for automated prostate segmentation in magnetic resonance.
abstract: BACKGROUND
  The prostate gland is delineated as the clinical target volume (CTV) in treatment planning of prostate cancer. Therefore, an accurate delineation is a prerequisite for efficient treatment. Accurate automated prostate segmentation methods facilitate the delineation of the CTV without inter-observer variation. The purpose of this study is to present an automated three-dimensional (3D) segmentation of the prostate using an active appearance model.
  
  
  METHODS
  Axial T2-weighted magnetic resonance (MR) scans were used to build the active appearance model. The model was based on a principal component analysis of shape and texture features with a level-set representation of the prostate shape instead of the selection of landmarks in the traditional active appearance model. To achieve a better fit of the model to the target image, prior knowledge to predict how to correct the model and pose parameters was incorporated. The segmentation was performed as an iterative algorithm to minimize the squared difference between the target and the model image.
  
  
  RESULTS
  The model was trained using manual delineations from 30 patients and was validated using leave-one-out cross validation where the automated segmentations were compared with the manual reference delineations. The mean and median dice similarity coefficient was 0.84 and 0.86, respectively.
  
  
  CONCLUSIONS
  This study demonstrated the feasibility for an automated prostate segmentation using an active appearance with results comparable to other studies.
authors.affiliation: Department of Health Science and Technology, Aalborg University , Aalborg , Denmark.
authors.forename: Anne Sofie
authors.initials: AS
authors.lastname: Korsager
authors.forename: Ulrik Landberg
authors.initials: UL
authors.lastname: Stephansen
authors.forename: Jesper
authors.initials: J
authors.lastname: Carl
authors.forename: Lasse Riis
authors.initials: LR
authors.lastname: Østergaard
mesh_terms: D000465:Algorithms; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000532:radiotherapy; D011880:Radiotherapy Planning, Computer-Assisted; D061089:Radiotherapy, Image-Guided