title: Segmentation of liver and spleen based on computational anatomy models.
abstract: Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs (p<0.00001).
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan. Electronic address: dongchunhua89@gmail.com.
authors.forename: Chunhua
authors.initials: C
authors.lastname: Dong
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan; College of Computer Science and Technology, Zhejiang University, Zhejiang, China. Electronic address: chen@is.ritsumei.ac.jp.
authors.forename: Yen-Wei
authors.initials: YW
authors.lastname: Chen
authors.affiliation: Biomedical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran.
authors.forename: Amir Hossein
authors.initials: AH
authors.lastname: Foruzan
authors.affiliation: College of Computer Science and Technology, Zhejiang University, Zhejiang, China.
authors.forename: Lanfen
authors.initials: L
authors.lastname: Lin
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan.
authors.forename: Xian-Hua
authors.initials: XH
authors.lastname: Han
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan.
authors.forename: Tomoko
authors.initials: T
authors.lastname: Tateyama
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan.
authors.forename: Xing
authors.initials: X
authors.lastname: Wu
authors.affiliation: Graduate School of Information Science and Engineering, Ritsumeikan University, Noji-higashi 1-1-1, Kusatsu, Japan.
authors.forename: Gang
authors.initials: G
authors.lastname: Xu
authors.affiliation: College of Software, Northeastern University, Shenyang, China.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Jiang
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D008953:Models, Anatomic; 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; D013154:Spleen / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Computational anatomy model; Iterative probabilistic atlas; Multiple organs segmentation; Organ bounding box; Template matching