A complete system for automatic extraction of left ventricular myocardium from CT images using shape segmentation and contour evolution.

The left ventricular myocardium plays a key role in the entire circulation system and an automatic delineation of the myocardium is a prerequisite for most of the subsequent functional analysis. In this paper, we present a complete system for an automatic segmentation of the left ventricular myocardium from cardiac computed tomography (CT) images using the shape information from images to be segmented. The system follows a coarse-to-fine strategy by first localizing the left ventricle and then deforming the myocardial surfaces of the left ventricle to refine the segmentation.

Automatic detection of left and right ventricles from CTA enables efficient alignment of anatomy with myocardial perfusion data.

Background: Accurate alignment between cardiac CT angiographic studies (CTA) and nuclear perfusion images is crucial for improved diagnosis of coronary artery disease. This study evaluated in an animal model the accuracy of a CTA fully automated biventricular segmentation algorithm, a necessary step for automatic and thus efficient PET/CT alignment.

Methods And Results: Twelve pigs with acute infarcts were imaged using Rb-82 PET and 64-slice CTA.

Symmetric fast marching schemes for better numerical isotropy.

Existing fast marching methods solve the Eikonal equation using a continuous (first-order) model to estimate the accumulated cost, but a discontinuous (zero-order) model for the traveling cost at each grid point. As a result the estimate of the accumulated cost (calculated numerically) at a given point will vary based on the direction of the arriving front, introducing an anisotropy into the discrete algorithm even though the continuous partial differential equation (PDE) is itself isotropic. To remove this anisotropy, we propose two very different schemes.

Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing.

Prognosis and diagnosis of cardiac diseases frequently require quantitative evaluation of the ventricle volume, mass, and ejection fraction. The delineation of the myocardial wall is involved in all of these evaluations, which is a challenging task due to large variations in myocardial shapes and image quality. In this paper, we present an automatic method for extracting the myocardial wall of the left and right ventricles from cardiac CT images.

Numerical conditioning problems and solutions for nonparametric i.i.d. statistical active contours.

In this paper, we propose an active contour model based on nonparametric independent and identically distributed (i.i.d.

Localized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approach.

We propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape.

Active Geodesics: Region-based Active Contour Segmentation with a Global Edge-based Constraint.

We present an contour model in which we constrain the evolving active contour to be a geodesic with respect to a weighted edge-based energy through its entire evolution rather than just at its final state (as in the traditional models). Since the contour is always a geodesic throughout the evolution, we automatically get local optimality with respect to an edge fitting criterion. This enables us to construct a purely region-based energy minimization model without having to devise arbitrary weights in the combination of our energy function to balance edge-based terms with the region-based terms.

A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors.

We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest.

Fully Isotropic Fast Marching Methods on Cartesian Grids.

The existing Fast Marching methods which are used to solve the Eikonal equation use a locally continuous model to estimate the accumulated cost, but a discontinuous (discretized) model for the traveling cost around each grid point. Because the accumulated cost and the traveling (local) cost are treated differently, the estimate of the accumulated cost at any point will vary based on the direction of the arriving front. Instead we propose to estimate the traveling cost at each grid point based on a locally continuous model, where we will interpolate the traveling cost along the direction of the propagating front.