title: Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images.
abstract: This paper presents an automated multi-organ segmentation method for 3D abdominal CT images based on a spatially-divided probabilistic atlases. Most previous abdominal organ segmentation methods are ineffective to deal with the large differences among patients in organ shape and position in local areas. In this paper, we propose an automated multi-organ segmentation method based on a spatially-divided probabilistic atlas, and solve this problem by introducing a scale hierarchical probabilistic atlas. The algorithm consists of image-space division and a multi-scale weighting scheme. The generated spatial-divided probabilistic atlas efficiently reduces the inter-subject variance in organ shape and position either in global or local regions. Our proposed method was evaluated using 100 abdominal CT volumes with manually traced ground truth data. Experimental results showed that it can segment the liver, spleen, pancreas, and kidneys with Dice similarity indices of 95.1%, 91.4%, 69.1%, and 90.1%, respectively.
authors.affiliation: Graduate School of Information Science, Nagoya University, Nagoya, Japan.
authors.forename: Chengwen
authors.initials: C
authors.lastname: Chu
authors.affiliation: Graduate School of Information Science, Nagoya University, Nagoya, Japan.
authors.forename: Masahiro
authors.initials: M
authors.lastname: Oda
authors.affiliation: Aichi Institute of Technology, Toyota, Japan.
authors.forename: Takayuki
authors.initials: T
authors.lastname: Kitasaka
authors.affiliation: Aichi Cancer Center Hospital, Nagoya, Japan.
authors.forename: Kazunari
authors.initials: K
authors.lastname: Misawa
authors.affiliation: Graduate School of Medicine, Nagoya University, Nagoya, Japan.
authors.forename: Michitaka
authors.initials: M
authors.lastname: Fujiwara
authors.affiliation: Information and Communications Headquarters, Nagoya University, Japan.
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Hayashi
authors.affiliation: Information and Communications Headquarters, Nagoya University, Japan.
authors.forename: Yukitaka
authors.initials: Y
authors.lastname: Nimura
authors.affiliation: Imperial College London, London, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
authors.affiliation: Information and Communications Headquarters, Nagoya University, Japan.
authors.forename: Kensaku
authors.initials: K
authors.lastname: Mori
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; 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*; D011860:Radiography, Abdominal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014781:Viscera / Q000000981:diagnostic imaging*