Automated segmentation of the left ventricle in cardiac MRI

Kaus, Michael R.; Berg, Jens von; Weese, Jürgen; Niessen, Wiro J.; Pekar, Vladimir

doi:10.1016/j.media.2004.06.015

Cited by 224 publications

(93 citation statements)

References 17 publications

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“…Kaus et al [17] combine a statistical model with coupled mesh surfaces for segmentation. However, their assumption that the heart is located in the center of the image is not always valid.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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“…However, there are some disadvantages related to the breath-hold 2D imaging sequences: a number of repeated breathholds are required to cover the whole heart and to acquire several imaging planes because of no availability for multiplanar reconstruction, and slices can be misregistered (9,10). The more images are needed, the longer the scan time should be, which leads to the lengthy examination time and change in image contrast because of the contrast washout.…”

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confidence: 99%

Free‐breathing high‐spatial‐resolution delayed contrast‐enhanced three‐dimensional viability MR imaging of the myocardium at 3.0T: A feasibility study

Amano

Matsumura

Kumita

2008

Magnetic Resonance Imaging

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Purpose:To assess the feasibility of free-breathing highspatial-resolution delayed contrast-enhanced three-dimensional (3D) viability magnetic resonance imaging (MRI) at 3.0T for the detection of myocardial damages. Materials and Methods:Twenty-five patients with myocardial diseases, including myocardial infarction and cardiomyopathies, were enrolled after informed consent was given. Free-breathing 3D viability MRI with high spatial resolution (1.5 ϫ 1.25 ϫ 2.5 mm) at 3.0T, for which cardiac and navigator gating techniques were employed, was compared with breath-hold two-dimensional (2D) viability imaging (1.77 ϫ 1.18 ϫ 10 mm) for assessment of contrastto-noise ratio (CNR) and myocardial damage.Results: Free-breathing 3D viability imaging was achieved successfully in 21 of the 25 patients. This imaging technique depicted 84.6% of hyperenhancing myocardium with a higher CNR between hyperenhancing myocardium and blood and with excellent agreement for the transmural extension of myocardial damage (k ϭ 0.91). In particular, the 3D viability images delineated the myocardial infarction and linear hyperenhancing myocardium, comparable to the 2D viability images. Conclusion:Free-breathing high-spatial-resolution delayed contrast-enhanced 3D viability MRI using 3.0T was feasible for the evaluation of hyperenhancing myocardium, as seen with myocardial infarction and cardiomyopathies.

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“…Kaus et al in [17] combine a statistical model with coupled mesh surfaces for segmentation. However, their assumption that the heart is located in the center of the image is not always valid.…”

Section: Knowledge-based Cardiac Segmentationmentioning

confidence: 99%

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a graphcut method to segment the cardiac right ventricle (RV) and left ventricle (LV) by using context information from each other. Contextual information is very helpful in medical image segmentation because the relative arrangement of different organs is the same. In addition to the conventional log-likelihood penalty, we also include a "context penalty" that captures the geometric relationship between the RV and LV. Contextual information for the RV is obtained by learning its geometrical relationship with respect to the LV. Similarly, RV provides geometrical context information for LV segmentation. The smoothness cost is formulated as a function of the learned context which helps in accurate labeling of pixels. Experimental results on real patient datasets from the STACOM database show the efficacy of our method in accurately segmenting the LV and RV. We also conduct experiments on simulated datasets to investigate our method's robustness to noise and inaccurate segmentations.

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Automated segmentation of the left ventricle in cardiac MRI

Cited by 224 publications

References 17 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Free‐breathing high‐spatial‐resolution delayed contrast‐enhanced three‐dimensional viability MR imaging of the myocardium at 3.0T: A feasibility study

Cardiac MRI Segmentation Using Mutual Context Information from Left and Right Ventricle

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