title: Abdominal multi-organ CT segmentation using organ correlation graph and prediction-based shape and location priors.
abstract: The paper addresses the automated segmentation of multiple organs in upper abdominal CT data. We propose a framework of multi-organ segmentation which is adaptable to any imaging conditions without using intensity information in manually traced training data. The features of the framework are as follows: (1) the organ correlation graph (OCG) is introduced, which encodes the spatial correlations among organs inherent in human anatomy; (2) the patient-specific organ shape and location priors obtained using OCG enable the estimation of intensity priors from only target data and optionally a number of untraced CT data of the same imaging condition as the target data. The proposed methods were evaluated through segmentation of eight abdominal organs (liver, spleen, left and right kidney, pancreas, gallbladder, aorta, and inferior vena cava) from 86 CT data obtained by four imaging conditions at two hospitals. The performance was comparable to the state-of-the-art method using intensity priors constructed from manually traced data.
authors: Department of Radiology, Graduate School of Medicine Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan. toshi@image.med.osaka-u.ac.jp
authors: Toshiyuki
authors: T
authors: Okada
authors: Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington DC, USA.
authors: Marius George
authors: MG
authors: Linguraru
authors: Department of Radiology, Graduate School of Medicine Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
authors: Masatoshi
authors: M
authors: Hori
authors: National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive Bethesda, MD 20892, USA.
authors: Ronald M
authors: RM
authors: Summers
authors: Department of Radiology, Graduate School of Medicine Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
authors: Noriyuki
authors: N
authors: Tomiyama
authors: Department of Radiology, Graduate School of Medicine Osaka University, 2-2 Yamadaoka, Suita, Osaka 565-0871, Japan.
authors: Yoshinobu
authors: Y
authors: Sato
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D008953:Models, Anatomic; 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; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*; D014584:User-Computer Interface; D014781:Viscera / Q000000981:diagnostic imaging*