title: Automated liver segmentation from a postmortem CT scan based on a statistical shape model.
abstract: OBJECTIVE
  Automated liver segmentation from a postmortem computed tomography (PMCT) volume is a challenging problem owing to the large deformation and intensity changes caused by severe pathology and/or postmortem changes. This paper addresses this problem by a novel segmentation algorithm using a statistical shape model (SSM) for a postmortem liver.
  
  
  METHODS
  The location and shape parameters of a liver are directly estimated from a given volume by the proposed SSM-guided expectation-maximization (EM) algorithm without any spatial standardization that might fail owing to the large deformation and intensity changes. The estimated location and shape parameters are then used as a constraint of the subsequent fine segmentation process based on graph cuts. Algorithms with eight different SSMs were trained using 144 in vivo and 32 postmortem livers, and the segmentation algorithm was tested on 32 postmortem livers in a twofold cross validation manner. The segmentation performance is measured by the Jaccard index (JI) between the segmentation result and the true liver label.
  
  
  RESULTS
  The average JI of the segmentation result with the best SSM was 0.8501, which was better compared with the results obtained using conventional SSMs and the results of the previous postmortem liver segmentation with statistically significant difference.
  
  
  CONCLUSIONS
  We proposed an algorithm for automated liver segmentation from a PMCT volume, in which an SSM-guided EM algorithm estimated the location and shape parameters of a liver in a given volume accurately. We demonstrated the effectiveness of the proposed algorithm using actual postmortem CT volumes.
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: Ai Information Center, Chuo-ku, Tokyo, Japan.
authors.forename: Seiji
authors.initials: S
authors.lastname: Yamamoto
authors.affiliation: Department of Radiology, International University of Health and Welfare, Mita Hospital, 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; D001344:Autopsy; D002102:Cadaver; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008099:Liver / Q000000981:diagnostic imaging*; D000069550:Machine Learning; D015233:Models, Statistical; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Autopsy imaging; EM algorithm; Liver segmentation; Postmortem CT; Statistical shape model; Synthesized-based learning