title: Myocardium Segmentation From DE MRI Using Multicomponent Gaussian Mixture Model and Coupled Level Set.
abstract: Objective:In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information.Methods:We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework.Results:The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods.Conclusion:The results can provide a benchmark for the myocardial segmentation in the literature.Significance:DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.Objective:In this paper, we propose a fully automatic framework for myocardium segmentation of delayed-enhancement (DE) MRI images without relying on prior patient-specific information.Methods:We employ a multicomponent Gaussian mixture model to deal with the intensity heterogeneity of myocardium caused by the infarcts. To differentiate the myocardium from other tissues with similar intensities, while at the same time maintain spatial continuity, we introduce a coupled level set (CLS) to regularize the posterior probability. The CLS, as a spatial regularization, can be adapted to the image characteristics dynamically. We also introduce an image intensity gradient based term into the CLS, adding an extra force to the posterior probability based framework, to improve the accuracy of myocardium boundary delineation. The prebuilt atlases are propagated to the target image to initialize the framework.Results:The proposed method was tested on datasets of 22 clinical cases, and achieved Dice similarity coefficients of 87.43 ± 5.62% (endocardium), 90.53 ± 3.20% (epicardium) and 73.58 ± 5.58% (myocardium), which have outperformed three variants of the classic segmentation methods.Conclusion:The results can provide a benchmark for the myocardial segmentation in the literature.Significance:DE MRI provides an important tool to assess the viability of myocardium. The accurate segmentation of myocardium, which is a prerequisite for further quantitative analysis of myocardial infarction (MI) region, can provide important support for the diagnosis and treatment management for MI patients.
authors.affiliation: School of Biomedical EngineeringShanghai Jiao Tong University.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Data ScienceFudan University.
authors.forename: Xiahai
authors.initials: X
authors.lastname: Zhuang
authors.affiliation: Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine.
authors.forename: Lianming
authors.initials: L
authors.lastname: Wu
authors.affiliation: Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine.
authors.forename: Dongaolei
authors.initials: D
authors.lastname: An
authors.affiliation: Department of RadiologyRenji HospitalShanghai Jiao Tong University School of Medicine.
authors.forename: Jianrong
authors.initials: J
authors.lastname: Xu
authors.affiliation: Robarts Research InstituteUniversity of Western Ontario.
authors.forename: Terry
authors.initials: T
authors.lastname: Peters
authors.affiliation: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Lixu
authors.initials: L
authors.lastname: Gu
mesh_terms: D000465:Algorithms; D006321:Heart / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016011:Normal Distribution
keywords: Force; Gaussian mixture model; Image segmentation; Level set; Magnetic resonance imaging; Myocardium