title: Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation.
abstract: One major limiting factor that prevents the accurate delineation of human organs has been the presence of severe pathology and pathology affecting organ borders. Overcoming these limitations is exactly what we are concerned in this study. We propose an automatic method for accurate and robust pathological organ segmentation from CT images. The method is grounded in the active shape model (ASM) framework. It leverages techniques from low-rank and sparse decomposition (LRSD) theory to robustly recover a subspace from grossly corrupted data. We first present a population-specific LRSD-based shape prior model, called LRSD-SM, to handle non-Gaussian gross errors caused by weak and misleading appearance cues of large lesions, complex shape variations, and poor adaptation to the finer local details in a unified framework. For the shape model initialization, we introduce a method based on patient-specific LRSD-based probabilistic atlas (PA), called LRSD-PA, to deal with large errors in atlas-to-target registration and low likelihood of the target organ. Furthermore, to make our segmentation framework more efficient and robust against local minima, we develop a hierarchical ASM search strategy. Our method is tested on the SLIVER07 database for liver segmentation competition, and ranks 3rd in all the published state-of-the-art automatic methods. Our method is also evaluated on some pathological organs (pathological liver and right lung) from 95 clinical CT scans and its results are compared with the three closely related methods. The applicability of the proposed method to segmentation of the various pathological organs (including some highly severe cases) is demonstrated with good results on both quantitative and qualitative experimentation; our segmentation algorithm can delineate organ boundaries that reach a level of accuracy comparable with those of human raters.
authors.affiliation: Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Commerce, Changsha 410205, China; School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Changfa
authors.initials: C
authors.lastname: Shi
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. Electronic address: yzcheng@hitwh.edu.cn.
authors.forename: Yuanzhi
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Jinke
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Information and Communications Headquarters, Nagoya University, Nagoya 464-8601, Japan.
authors.forename: Kensaku
authors.initials: K
authors.lastname: Mori
authors.affiliation: Department of Radiology, Graduate School of Medicine, Osaka University, Suita 565-0871, Japan.
authors.forename: Shinichi
authors.initials: S
authors.lastname: Tamura
mesh_terms: D000465:Algorithms; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging* / Q000473:pathology*; D008168:Lung / Q000000981:diagnostic imaging* / Q000473:pathology*; D011336:Probability; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Active shape model; Hierarchical model; Low-rank and sparse decomposition; Pathological organ segmentation; Probabilistic atlas