title: Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps.
abstract: In this paper we present a model for describing the position distribution of the endocardium in the two-chamber apical long-axis view of the heart in clinical B-mode ultrasound cycles. We propose a novel Bayesian formulation, including priors for spatial and temporal smoothness, and preferred shapes and position. The shape model takes into account both endocardium, atrial region and apex. The likelihood is built using a statistical signal model, which attempts to closely model a censored signal. In addition, the use of a censored Gamma mixture model with unknown censoring point, to handle artefacts resulting from left-censoring of the in US clinical B-mode, is to our knowledge novel. The posterior density is sampled by the Gibbs method to estimate the expected latent variable representation of the endocardium, which we call the Bayesian Probability Map; the map describes the probability of pixels being classified as being within the endocardium. The regularization parameters of the model are estimated by cross-validation, and the results are compared against the two-chamber apical model of Chen et al.
authors: Image Group, Department of Computer Science, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark. Electronic address: nmhansson@di.ku.dk.
authors: Mattias
authors: M
authors: Hansson
authors: Image Group, Department of Computer Science, University of Copenhagen, Universitetsparken 5, 2100 København Ø, Denmark.
authors: Sami S
authors: SS
authors: Brandt
authors: Mathematical Statistics, Centre for Mathematical Sciences, Lund University, Lund, Sweden.
authors: Johan
authors: J
authors: Lindström
authors: Faculty of Health and Society, Malmö University, Malmö, Sweden.
authors: Petri
authors: P
authors: Gudmundsson
authors: Department of Heart Failure and Valvular Disease, Skåne University Hospital, Malmö, Sweden.
authors: Amra
authors: A
authors: Jujić
authors: Department of Heart Failure and Valvular Disease, Skåne University Hospital, Malmö, Sweden.
authors: Andreas
authors: A
authors: Malmgren
authors: Faculty of Technology and Society, Malmö University, Malmö, Sweden.
authors: Yuanji
authors: Y
authors: Cheng
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D004452:Echocardiography / Q000379:methods*; D004699:Endocardium / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: B-mode ultrasound; Bayes; Cardiac; Censored data; Segmentation