The Automatic Identification of Hibernating Myocardium

Noble, Nicholas M. I.; Hill, Derek L. G.; Breeuwer, Marcel; Razavi, Reza

doi:10.1007/978-3-540-30136-3_108

Cited by 15 publications

(11 citation statements)

References 13 publications

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“…Some of the approaches aim at coronary tree analysis [5][6][7], late enhancement detection [8,9], and analysis of time intensity curves [10,11]. These methods use low-level information from the data without incorporating prior knowledge.…”

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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“…While polar plots are commonly used in nuclear cardiology, these plots are only sporadically used in cardiac MRI today [20] [21]. Because co-registered morphological MR images are by default available in PET/MRI, we performed segmentation of myocardial boundaries in MR images.…”

Section: Discussionmentioning

confidence: 99%

Integrated Assessment of Cardiac PET/MRI: Co-Registered PET and MRI Polar Plots by Mutual MR-Based Segmentation of the Left Ventricular Myocardium

Nensa¹,

Poeppel²,

Tezgah³

et al. 2017

WJCD

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Background: In the present study, we sought to describe a procedure for the creation of co-registered positron emission tomography (PET) and magnetic resonance imaging (MRI) polar plots of cardiac PET/MRI examinations, validate the resulting plots against available standard methods in patients with myocardial infarction and provide examples that demonstrate the advantage of the novel approach over existing standards. Methods: Co-registered LGE and PET short-axis images were transformed into polar maps based on a radial sampling pattern. LGE was automatically detected using an automated thresholding algorithm (ATA). In 20 PET/MRI examinations in patients with acute myocardial infarction, agreement between manual LGE assessment and the ATA classification was calculated. Also agreement between MRI-segmentation based PET polar plots and standard PET polar plots (created with the Corridor4DM software package) was assessed. Results: No statistically significant difference in infarct sizes between manual and ATA segmentation was found (p = 0.12). Both methods were highly correlated (Pearson's r = 0.96, p < 0.01). Bland-Altman analysis revealed lower and upper limits of agreement of −4.7 g and 3.6 g. Agreement between MRI-segmentation based PET polar plots and standard PET polar plots was very high (mean kappa of 0.91 ± 0.10; p < 0.01 in all cases). In three additional patients with myocardial inflammation, the software successfully created polar plots that demonstrate How to cite this paper: Nensa, F., Poeppel, T.D., Tezgah, E., Heusch, P., Nassenstein, K., Forsting, M., Bockisch, A., Erbel, R. and 92the location and extent of pathologic tracer uptake in the left ventricular myocardium. Conclusion: A straightforward software approach for the creation of co-registered PET and MRI polar plots was described and successfully demonstrated in PET/MRI studies of myocardial infarction and inflammation.

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“…However, automatically performing this task is challenging and, to our knowledge, not yet offered by any commercial product. Indeed, recent studies involving viability assessment generally use a manual or semi-automatic method to obtain the myocardial contours [1,2]. Even the numerous publications that describe automatic methods for myocardium delineation, involving shape and appearance models [3,4], deformable meshes [4], level sets [5,6] or graph-cuts [7], are designed for cine images.…”

Section: Fig 1 Sample Short-axis Le Cmr Slicementioning

confidence: 99%

Automatic myocardium segmentation in late-enhancement MRI

Ciofolo

Fradkin

Mory

et al. 2008

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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We propose a novel automatic method to segment the myocardium on late-enhancement cardiac MR (LE CMR) images with a multi-step approach. First, in each slice of the LE CMR volume, a geometrical template is deformed so that its borders fit the myocardial contours. The second step consists in introducing a shape prior of the left ventricle. To do so, we use the cine MR sequence that is acquired along with the LE CMR volume. As the myocardial contours can be more easily automatically obtained on this data, they are used to build a 3D mesh representing the left ventricle geometry and the underlying myocardium thickness. This mesh is registered towards the contours obtained with the geometrical template, then locally adjusted to guarantee that scars are included inside the final segmentation. The quantitative evaluation on 27 volumes (272 slices) shows robust and accurate results.

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The Automatic Identification of Hibernating Myocardium

Cited by 15 publications

References 13 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

Integrated Assessment of Cardiac PET/MRI: Co-Registered PET and MRI Polar Plots by Mutual MR-Based Segmentation of the Left Ventricular Myocardium

Automatic myocardium segmentation in late-enhancement MRI

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