title: Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs.
abstract: The goal of this study is to provide a theoretical framework for accurately optimizing the segmentation energy considering all of the possible shapes generated from the level-set-based statistical shape model (SSM). The proposed algorithm solves the well-known open problem, in which a shape prior may not be optimal in terms of an objective functional that needs to be minimized during segmentation. The algorithm allows the selection of an optimal shape prior from among all possible shapes generated from an SSM by conducting a branch-and-bound search over an eigenshape space. The proposed algorithm does not require predefined shape templates or the construction of a hierarchical clustering tree before graph-cut segmentation. It jointly optimizes an objective functional in terms of both the shape prior and segmentation labeling, and finds an optimal solution by considering all possible shapes generated from an SSM. We apply the proposed algorithm to both pancreas and spleen segmentation using multiphase computed tomography volumes, and we compare the results obtained with those produced by a conventional algorithm employing a branch-and-bound search over a search tree of predefined shapes, which were sampled discretely from an SSM. The proposed algorithm significantly improves the segmentation performance in terms of the Jaccard index and Dice similarity index. In addition, we compare the results with the state-of-the-art multiple abdominal organs segmentation algorithm, and confirmed that the performances of both algorithms are comparable to each other. We discuss the high computational efficiency of the proposed algorithm, which was determined experimentally using a normalized number of traversed nodes in a search tree, and the extensibility of the proposed algorithm to other SSMs or energy functionals.
authors.affiliation: Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan. Electronic address: 50013834203@st.tuat.ac.jp.
authors.forename: Atsushi
authors.initials: A
authors.lastname: Saito
authors.affiliation: Center for Radiological Sciences, International University of Health and Welfare, 1-4-3 Mita, Minato-ku, Tokyo 108-8329, Japan . Electronic address: snawano@iuhw.ac.jp.
authors.forename: Shigeru
authors.initials: S
authors.lastname: Nawano
authors.affiliation: Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan. Electronic address: simiz@cc.tuat.ac.jp.
authors.forename: Akinobu
authors.initials: A
authors.lastname: Shimizu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D015233:Models, Statistical; D010179:Pancreas / Q000000981:diagnostic imaging*; 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*; D014781:Viscera / Q000000981:diagnostic imaging
keywords: Branch-and-bound search; Computed tomography; Graph cuts; Segmentation; Statistical shape model