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:
https://lup.lub.lu.se/record/4615988
- author
- Hansson, Mattias
; Brandt, Sami S
; Lindström, Johan
LU
; Gudmundsson, Petri
; Jujic, Amra
LU
; Malmgren, Andreas
LU
and Cheng, Yuanji
- organization
- publishing date
- 2014
- 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
- pmid:25077846
- ISSN
- 1361-8415
- DOI
- 10.1016/j.media.2014.06.004
- 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
- 2016-04-01 10:12:21
- date last changed
- 2022-04-04 03:25:31
@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}},
doi = {{10.1016/j.media.2014.06.004}},
volume = {{18}},
year = {{2014}},
}