Automated Segmentation of the Left Ventricle Including Papillary Muscles in Cardiac Magnetic Resonance Images

Berbari, R. El; Bloch, Isabelle; Redheuil, Alban; Angelini, Elsa D.; Mousseaux, Élie; Frouin, Frédérique; Herment, A.

doi:10.1007/978-3-540-72907-5_46

Cited by 11 publications

(14 citation statements)

References 16 publications

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“…Five parameters control the deformation process of the snake: α and β, that control respectively the elasticity of the contour and its rigidity, κ p and κ that are weights of a pressure force P orthogonal to the contour and a Gradient Vector Flow force, respectively, and µ gvf that is the regularization parameter of the GVF. The whole set of images was processed using the same values of the snake parameters [2]. A high value of the rigidity parameter β at this first step can guarantee a rigid contour.…”

Section: Endocardial Delineationmentioning

confidence: 99%

“…Encouraging results obtained in this feasibility study constitute a motivation to pursue this idea and improve the segmentation step making it more robust and automatic. In [2], the endocardial delineation step was improved in order to overcome problems with papillary muscles and poor homogeneity of the cavity due to blood flow. In this paper the segmentation process is extended to the epicardial border.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An automated myocardial segmentation in cardiac MRI

Berbari

Bloch

Redheuil

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In this paper we present an automatic approach to segment Cardiac Magnetic Resonance (CMR) images. A preprocessing step that consists in filtering the image using connected operators (area opening and closing filters) is applied in order to homogenize the cavity and solve the problems due to the papillary muscles. Thereby the GVF snake algorithm is applied with one point clicked in the cavity as initialization and an optimized tuning of parameters for the endocardial contour extraction. The epicardial border is then obtained using the endocardium as initialization. The performance of the proposed method was assessed by experimentation on thirtynine CMR images. A high agreement between manual and automatic contours was obtained with correlation scores of 0.96 for the endocardium and 0.90 for the epicardium. Overlapping percentage, mean and maximum distances between the two contours show a good performance of the method.

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Section: Endocardial Delineationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An automated myocardial segmentation in cardiac MRI

Berbari

Bloch

Redheuil

et al. 2007

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Many techniques are available to diagnose heart diseases. CT, Nuclear Medicine, Ultrasound and MRI are the most common cardiac imaging techniques [1]. Cardiac magnetic resonance imaging (CMR) is a rapidly evolving field which provides high contrast and high resolution 3-dimensional images of the heart, coronary vessels, and the great vessels without subjecting the patient to ionizing radiation.…”

Section: Introductionmentioning

confidence: 99%

A Brief Study on Automated Non Contrast Cardiac MRI Segmentation by Machine Vision Techniques

Raman¹,

Then²,

Rapaee³

2012

IJET

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“…Thus, the first contour was used as initialization of a new segmentation, applied to the gradient of the original image with lower values of β = 5, κ p = 0.1 and µ gvf = 0.1. This segmentation method was validated on control subjects and patients [7,11].…”

Section: Automated Endocardial Delineationmentioning

confidence: 99%

An automated evaluation of regional left ventricular function on cine magnetic resonance images

Berbari

Kachenoura

Redheuil

et al. 2008

2008 Computers in Cardiology

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Routine evaluation of the regional ventricular function in cardiac magnetic resonance (CMR) remains mostly based on visual analysis of cine loops and subsequent interobserver variability remains a potential drawback for diagnosis and follow-up of coronary artery disease (CAD). In this paper we propose a method that ensures automated assessment of normalized mean contraction times and radial endocardial velocities from MR cine images, by combining a parametric imaging analysis to an automatic segmentation method of the left ventricle. Short-axis cine loops were obtained in 20 subjects (10 myocardial infarction (MI) and 10 control). Reference segmental wall motion was scored by an expert according to 4 classes (normokinesia, hypokinesia, akinesia and dyskinesia). Results showed increased values for normalized mean contraction times and decreased values for the radial velocity in segments with wall motion abnormalities (WMA) compared to segments with normal function. These functional parameters could help to detect regional WMA.

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Automated Segmentation of the Left Ventricle Including Papillary Muscles in Cardiac Magnetic Resonance Images

Cited by 11 publications

References 16 publications

An automated myocardial segmentation in cardiac MRI

An automated myocardial segmentation in cardiac MRI

A Brief Study on Automated Non Contrast Cardiac MRI Segmentation by Machine Vision Techniques

An automated evaluation of regional left ventricular function on cine magnetic resonance images

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