title: Constructing a probabilistic model for automated liver region segmentation using non-contrast X-ray torso CT images.
abstract: A probabilistic model was proposed in this research for fully-automated segmentation of liver region in non-contrast X-ray torso CT images. This probabilistic model was composed of two kinds of probability that show the location and density (CT number) of the liver in CT images. The probability of the liver on the spatial location was constructed from a number of CT scans in which the liver regions were pre-segmented manually as gold standards. The probability of the liver on density was estimated specifically using a Gaussian function. The proposed probabilistic model was used for automated liver segmentation from non-contrast CT images. 132 cases of the CT scans were used for the probabilistic model construction and then this model was applied to segment liver region based on a leave-one-out method. The performances of the probabilistic model were evaluated by comparing the segmented liver with the gold standard in each CT case. The validity and usefulness of the proposed model were proved.
authors.affiliation: Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu 501-1194, Japan. zxr@fjt.info.gifu-u.ac.jp
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Zhou
authors.forename: Teruhiko
authors.initials: T
authors.lastname: Kitagawa
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hara
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Fujita
authors.forename: Xuejun
authors.initials: X
authors.lastname: Zhang
authors.forename: Ryujiro
authors.initials: R
authors.lastname: Yokoyama
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Kondo
authors.forename: Masayuki
authors.initials: M
authors.lastname: Kanematsu
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Hoshi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003287:Contrast Media; D005234:Fatty Liver / Q000000981:diagnostic imaging*; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media