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Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps

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Author

Summary, in English

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.

Publishing year

2014

Language

English

Pages

1184-1199

Publication/Series

Medical Image Analysis

Volume

18

Issue

7

Document type

Journal article

Publisher

Elsevier

Topic

  • Radiology, Nuclear Medicine and Medical Imaging

Status

Published

Research group

  • Cardiovascular Research - Hypertension
  • Cardiology Research Group

ISBN/ISSN/Other

  • ISSN: 1361-8415