Fig 1 - uploaded by Petri Gudmundsson
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Location prior (green) for a frame in an ultrasound sequence. The probability of a latent variable being sampled as endocardium is zero outside the colored area.
Source publication
In this paper we present improvements to our Bayesian approach for describing the position distribution of the endocardium
in cardiac ultrasound image sequences. The problem is represented as a latent variable model, which represents the inside
and outside of the endocardium, for which the posterior density is estimated. We start our construction b...
Citations
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.
In this paper, we propose a sampling-based shape segmentation method that builds upon a global shape and a local appearance
model. It is suited for challenging problems where there is high uncertainty about the correct solution due to a low signal-to-noise
ratio, clutter, occlusions or an erroneous model. Our method suits for segmentation tasks where the number of objects is not
known a priori, or where the object of interest is invisible and can only be inferred from other objects in the image. The
method was inspired by shape particle filtering from de Bruijne and Nielsen, but shows substantial improvements to it. The
principal contributions of this paper are as follows: (i) We introduce statistically motivated importance weights that lead
to better performance and facilitate the application to new problems. (ii) We adapt the static sequential Monte Carlo (SMC)
algorithm to the problem of image segmentation, where the algorithm proves to sample efficiently from high-dimensional static
spaces. (iii) We evaluate the static SMC sampler on shapes on a medical problem of high relevance: the automated quantification
of aortic calcifications on X-ray radiographs for the prognosis and diagnosis of cardiovascular disease and mortality. Our
results suggest that the static SMC sampler on shapes is more generic, robust, and accurate than shape particle filtering,
while being computationally equally costly.
We present a fully automated framework for scoring a patient's risk of cardiovascular disease (CVD) and mortality from a standard lateral radiograph of the lumbar aorta. The framework segments abdominal aortic calcifications for computing a CVD risk score and performs a survival analysis to validate the score. Since the aorta is invisible on X-ray images, its position is reasoned from 1) the shape and location of the lumbar vertebrae and 2) the location, shape, and orientation of potential calcifications. The proposed framework follows the principle of Bayesian inference, which has several advantages in the complex task of segmenting aortic calcifications. Bayesian modeling allows us to compute CVD risk scores conditioned on the seen calcifications by formulating distributions, dependencies, and constraints on the unknown parameters. We evaluate the framework on two datasets consisting of 351 and 462 standard lumbar radiographs, respectively. Promising results indicate that the framework has potential applications in diagnosis, treatment planning, and the study of drug effects related to CVD.
