Advanced

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

Hansson, Mattias; Brandt, Sami S; Lindström, Johan LU ; Gudmundsson, Petri; Jujic, Amra LU ; Malmgren, Andreas LU and Cheng, Yuanji (2014) In Medical Image Analysis 18(7). p.1184-1199
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Medical Image Analysis
volume
18
issue
7
pages
1184 - 1199
publisher
Elsevier
external identifiers
  • pmid:25077846
  • wos:000342256200019
  • scopus:84904873952
ISSN
1361-8415
DOI
10.1016/j.media.2014.06.004
project
BECC
language
English
LU publication?
yes
id
8431940b-3446-40cd-9efd-a33142e0a1bc (old id 4615988)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/25077846?dopt=Abstract
date added to LUP
2014-09-02 18:56:08
date last changed
2017-11-12 03:05:04
@article{8431940b-3446-40cd-9efd-a33142e0a1bc,
  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.},
  author       = {Hansson, Mattias and Brandt, Sami S and Lindström, Johan and Gudmundsson, Petri and Jujic, Amra and Malmgren, Andreas and Cheng, Yuanji},
  issn         = {1361-8415},
  language     = {eng},
  number       = {7},
  pages        = {1184--1199},
  publisher    = {Elsevier},
  series       = {Medical Image Analysis},
  title        = {Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps},
  url          = {http://dx.doi.org/10.1016/j.media.2014.06.004},
  volume       = {18},
  year         = {2014},
}