We propose a method for segmentation of the left ventricle in magnetic resonance cardiac images. The framework consists of an initial Bayesian segmentation of the central slice of the volume. This segmentation is used to locate a shape prior for the LV myocardial tissue. This shape prior is determined using the fact that the myocardium is approximately annular as seen in the short-axis. Then a second Bayesian segmentation is performed to obtain the final result. This procedure is repeated for the rest of the slices. An extrapolation of the area of the LV is used to determine a stopping criterion. The method was evaluated on the databases of the Cardiac Atlas project. Our results demonstrate a suitable accuracy for myocardial segmentation (≈...
Cardiac MR image segmentation is essential for the morphological and functional analysis of the hear...
© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morpholog...
We evaluate in this paper different strategies for the construction of a statistical shape model (SS...
In this paper, we propose a data-driven approach that extracts prior information for segmentation of...
In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, an...
Cette thèse a pour objectif de parvenir à une estimation automatisée des contours du ventricule gauc...
The aim of this work is to perform an automated segmentation of the Left Ventricle on short-axis car...
Object: We present in this paper the application of a statistical shape model of the left ventricle ...
Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR)...
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and en...
In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left...
Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPEC...
This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the ...
Cardiac magnetic resonance imaging is the golden standard for the quantification of left ventricular...
Segmentation of left ventricles in Cine MR images plays an important role in analyzing cardiac funct...
Cardiac MR image segmentation is essential for the morphological and functional analysis of the hear...
© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morpholog...
We evaluate in this paper different strategies for the construction of a statistical shape model (SS...
In this paper, we propose a data-driven approach that extracts prior information for segmentation of...
In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, an...
Cette thèse a pour objectif de parvenir à une estimation automatisée des contours du ventricule gauc...
The aim of this work is to perform an automated segmentation of the Left Ventricle on short-axis car...
Object: We present in this paper the application of a statistical shape model of the left ventricle ...
Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR)...
A novel approach for the automatic segmentation has been developed to extract the epi-cardium and en...
In this paper, a new segmentation framework with prior knowledge is proposed and applied to the left...
Accurate segmentation of the left ventricle (LV) is crucial for evaluating myocardial perfusion SPEC...
This paper presents a coupled level-set segmentation of the myocardium of the left ventricle of the ...
Cardiac magnetic resonance imaging is the golden standard for the quantification of left ventricular...
Segmentation of left ventricles in Cine MR images plays an important role in analyzing cardiac funct...
Cardiac MR image segmentation is essential for the morphological and functional analysis of the hear...
© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morpholog...
We evaluate in this paper different strategies for the construction of a statistical shape model (SS...