Cardiac segmentation by a velocity-aided active contour model

Benkeser, P.J.

doi:10.1016/j.compmedimag.2005.10.006

Cited by 21 publications

(21 citation statements)

References 12 publications

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“…Montillo et al [12] and Young et al [13] described fully automated and semi-automated segmentation methods [12,13], respectively, using an LV finite element model. In PC velocity encoding CMR, active contour models and the velocity phase data are used to distinguish between myocardium and blood [14]. Kainmüller et al introduced a method that uses edge detection, curvature, flow and prior shape information [15].…”

Section: Introductionmentioning

confidence: 99%

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

Auger

Zhong

Epstein

et al. 2014

Journal of Cardiovascular Magnetic Resonance

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BackgroundThe most time consuming and limiting step in three dimensional (3D) cine displacement encoding with stimulated echoes (DENSE) MR image analysis is the demarcation of the left ventricle (LV) from its surrounding anatomical structures. The aim of this study is to implement a semi-automated segmentation algorithm for 3D cine DENSE CMR using a guide point model approach.MethodsA 3D mathematical model is fitted to guide points which were interactively placed along the LV borders at a single time frame. An algorithm is presented to robustly propagate LV epicardial and endocardial surfaces of the model using the displacement information encoded in the phase images of DENSE data. The accuracy, precision and efficiency of the algorithm are tested.ResultsThe model-defined contours show good accuracy when compared to the corresponding manually defined contours as similarity coefficients Dice and Jaccard consist of values above 0.7, while false positive and false negative measures show low percentage values. This is based on a measure of segmentation error on intra- and inter-observer spatial overlap variability. The segmentation algorithm offers a 10-fold reduction in the time required to identify LV epicardial and endocardial borders for a single 3D DENSE data set.ConclusionA semi-automated segmentation method has been developed for 3D cine DENSE CMR. The algorithm allows for contouring of the first cardiac frame where blood-myocardium contrast is almost nonexistent and reduces the time required to segment a 3D DENSE data set significantly.

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Section: Introductionmentioning

confidence: 99%

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

Auger

Zhong

Epstein

et al. 2014

Journal of Cardiovascular Magnetic Resonance

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“…Algorithms that utilize the phase in MRI have either relied on statistical models [12], used the phase to propagate a manually drawn contour throughout the cardiac phases cycle [13], or used the phase to account for image distortions inherent to MRI [14][15][16]. Incorporation of phase data into active models was proposed by Cho et al [17]. In their work PC-MRI data was incorporated as an external force in an active contour that was attracted to changes in velocity orientations.…”

Section: Introductionmentioning

confidence: 99%

Velocity guided segmentation of phase contrast magnetic resonance angiography

Janiczek

Epstein

Acton

2008

2008 15th IEEE International Conference on Image Processing

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Atherosclerosis, the precursor to acute events such as myocardial infarction and stroke, is a focal inflammatory disease that is influenced by local hemodynamic forces such as wall shear stress (WSS). Phase contrast (PC) MRI is an established method that encodes blood velocity into the phase of an image. A requisite step for the calculation of WSS from PC-MRI data is delineation of the vasculature. Previous automatic segmentation algorithms have primarily relied on the image magnitude. Phase based segmentation algorithms have reduced the phase data to image features prior to segmentation. We outline a novel method that directly incorporates phase data as an additional external force in an active model. We demonstrate improved segmentation results over current methods on both synthetic and real data.

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“…Cho et al used a tensor-based orientation gradient force (OGF) for endocardial PC MRI segmentation 16,17 . Other techniques have made use of Bayesian statistical frameworks involving all three components of velocities in conjunction with magnitude or speed images.…”

Section: Introductionmentioning

confidence: 99%

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

Sundareswaran

Yoganathan

2008

SPIE Proceedings

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Recent technological advances have contributed to the advent of phase contrast magnetic resonance imaging (PCMRI) as standard practice in clinical environments. In particular, decreased scan times have made using the modality more feasible. PCMRI is now a common tool for flow quantification, and for more complex vector field analyses that target the early detection of problematic flow conditions. Segmentation is one component of this type of application that can impact the accuracy of the final product dramatically. Vascular segmentation, in general, is a long-standing problem that has received significant attention. Segmentation in the context of PCMRI data, however, has been explored less and can benefit from object-based image processing techniques that incorporate fluids specific information. Here we present a fuzzy rule-based adaptive vector median filtering (FAVMF) algorithm that in combination with active contour modeling facilitates high-quality PCMRI segmentation while mitigating the effects of noise.The FAVMF technique was tested on 111 synthetically generated PC MRI slices and on 15 patients with congenital heart disease. The results were compared to other multi-dimensional filters namely the adaptive vector median filter, the adaptive vector directional filter, and the scalar low pass filter commonly used in PC MRI applications. FAVMF significantly outperformed the standard filtering methods (p < 0.0001). Two conclusions can be drawn from these results: a) Filtering should be performed after vessel segmentation of PC MRI; b) Vector based filtering methods should be used instead of scalar techniques.

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Cardiac segmentation by a velocity-aided active contour model

Cited by 21 publications

References 12 publications

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

Semi-automated left ventricular segmentation based on a guide point model approach for 3D cine DENSE cardiovascular magnetic resonance

Velocity guided segmentation of phase contrast magnetic resonance angiography

Rule-based fuzzy vector median filters for 3D phase contrast MRI segmentation

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