TY - JOUR
T1 - Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps
AU - Hansson, Nils Mattias
AU - Brandt, Sami Sebastian
AU - Lindström, Johan
AU - Gudmundsson, Petri
AU - Jujić, Amra
AU - Malmgren, Andreas
AU - Cheng, Yuanji
PY - 2014/10
Y1 - 2014/10
N2 - 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.
AB - 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.
U2 - 10.1016/j.media.2014.06.004
DO - 10.1016/j.media.2014.06.004
M3 - Journal article
SN - 1361-8415
VL - 18
SP - 1184
EP - 1199
JO - Medical Image Analysis
JF - Medical Image Analysis
IS - 7
ER -