title: Fast approximation for joint optimization of segmentation, shape, and location priors, and its application in gallbladder segmentation.
abstract: OBJECTIVE
  This paper addresses joint optimization for segmentation and shape priors, including translation, to overcome inter-subject variability in the location of an organ. Because a simple extension of the previous exact optimization method is too computationally complex, we propose a fast approximation for optimization. The effectiveness of the proposed approximation is validated in the context of gallbladder segmentation from a non-contrast computed tomography (CT) volume.
  
  
  METHODS
  After spatial standardization and estimation of the posterior probability of the target organ, simultaneous optimization of the segmentation, shape, and location priors is performed using a branch-and-bound method. Fast approximation is achieved by combining sampling in the eigenshape space to reduce the number of shape priors and an efficient computational technique for evaluating the lower bound.
  
  
  RESULTS
  Performance was evaluated using threefold cross-validation of 27 CT volumes. Optimization in terms of translation of the shape prior significantly improved segmentation performance. The proposed method achieved a result of 0.623 on the Jaccard index in gallbladder segmentation, which is comparable to that of state-of-the-art methods. The computational efficiency of the algorithm is confirmed to be good enough to allow execution on a personal computer.
  
  
  CONCLUSIONS
  Joint optimization of the segmentation, shape, and location priors was proposed, and it proved to be effective in gallbladder segmentation with high computational efficiency.
authors.affiliation: Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan. a-saito@go.tuat.ac.jp.
authors.forename: Atsushi
authors.initials: A
authors.lastname: Saito
authors.affiliation: Department of Radiology, Mita Hospital, International University of Health and Welfare, Minato-ku, Tokyo, Japan.
authors.forename: Shigeru
authors.initials: S
authors.lastname: Nawano
authors.affiliation: Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan.
authors.forename: Akinobu
authors.initials: A
authors.lastname: Shimizu
mesh_terms: D000465:Algorithms; D005704:Gallbladder / Q000000981:diagnostic imaging*; D006801:Humans; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Branch-and-bound method; Computed tomography; Gallbladder segmentation; Graph cuts; Statistical shape model