Segmentation of the Heart Muscle in 3-D Pediatric Echocardiographic Images

Nillesen, Maartje M.; Lopata, R.G.P.; Gerrits, I.H.; Kapusta, Livia; Huisman, Henkjan; Thijssen, J.M.; Korte, Chris L. de

doi:10.1016/j.ultrasmedbio.2007.04.001

Cited by 32 publications

(21 citation statements)

References 30 publications

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“…The external force, consisting of a gradient and speed force, is derived from the image data and steers the simplex mesh onto boundary structures [5,8]. Both adaptive filtered data (AMS) [9] as well as optimal MCC-values obtained in part A, were used to compute the external force.…”

Section: B Segmentationmentioning

confidence: 99%

Correlation-based discrimination between myocardial tissue and blood in 3D echocardiographic images

Saris

Nillesen

Lopata

et al. 2012

2012 IEEE International Ultrasonics Symposium

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The aim of this study was to investigate the merit of using temporal cross-correlation between subsequent echo volumes for discriminating blood and myocardial tissue, to facilitate automated segmentation of 3D echocardiographic images over the entire cardiac cycle. Because of poor echogenicity contrast between blood and myocardial tissue in some regions and the presence of speckle noise, automated segmentation is challenging. For patients with a congenital heart disease, segmentation should rely on echo features solely as incorporation of a priori knowledge of the shape of the heart is undesirable. Therefore, we analyzed the performance of a 3D iterative cross-correlation algorithm to obtain optimal contrast of maximum cross-correlation (MCC) values between blood and myocardial tissue in all phases of the cardiac cycle. Both contrast and boundary-gradient quality measures were assessed to optimize the MCC-values with respect to signal choice (radiofrequency (RF) data or envelope data) and axial window size. Results in 3D echocardiographic image sequences of five healthy children demonstrate that the use of envelope data outperformed the use of RF-data in terms of optimal blood-myocardium CNR, Overlap and Acutance, in all phases of the cardiac cycle (p < 0.05). The use of a relatively small axial window (0.7 -1.25 mm) at fine-scale resulted in optimal contrast and boundary-gradient between the two tissues. Optimal MCC-values, either alone or in combination with adaptive filtered, demodulated RF-data, were used as additional external force in a deformable model to segment the left ventricular cavity in one dataset. Incorporation of MCC-values had additional value for automated segmentation. Index Terms -3D echocardiography, temporal cross-correlation, image segmentation, deformable model, congenital heart disease.

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Section: B Segmentationmentioning

confidence: 99%

Correlation-based discrimination between myocardial tissue and blood in 3D echocardiographic images

Saris

Nillesen

Lopata

et al. 2012

2012 IEEE International Ultrasonics Symposium

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“…In contrary to strain imaging, not the position of the peak of the cross-correlation function is used but the maximum value of this function (MCC). The envelope of the RF datasets was first processed with an Adaptive Mean Squared (AMS) filter that reduces the speckle pattern in the blood and heart muscle but preserves the transition between these two [15]. The AMS filtered values and the MCC values were both used as external force in a gradient-based deformable simplex mesh model, i.e., AMS and MCC values were used to compute gradient and speed forces [16].…”

Section: Data Processingmentioning

confidence: 99%

Functional cardiovascular ultrasound imaging: Quantification of plaque vulnerability and cardiac function

Korte

Lopata

Hansen

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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With ultrasound strain imaging, the function of tissue and organs can be identified. The technique uses multiple images, acquired from tissue under different degrees of deformation. We developed techniques for cardiovascular applications.The displacement of tissue can be determined at micrometer scale using the raw (i.e., radio frequency, RF-) ultrasound data, containing the amplitude as well as the phase information. A 2D/3D cross-correlation based coarse-to-fine strain estimation strategy is used to quantify the strain. The maximum value of the cross-correlation function is also used for automated segmentation. Furthermore, strain estimation is improved by strain compounding, a technique that combines data acquired at multiple beam-steered angles.Validation experiments in phantoms demonstrated that accurate strain images can be determined using the proposed technique and that strain compounding improves the strain estimate in directions perpendicular to the ultrasound beam. Evaluation using cardiac data in animals showed that automated segmentation provides accurate quantification of the cardiac output and that the strain in the heart walls can be estimated in three dimensions. Segmentation based on combining temporal correlation and filtered echo level outperforms segmentation based on echo level alone.

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“…speckle noise is reduced, whereas in inhomogeneous regions the degree of filtering is low, such that transitions between blood and myocardium are preserved. The AMS filter has been proven to be effective for segmentation of echocardiographic images when using gradient based deformable models [7].…”

Section: D Adaptive Filteringmentioning

confidence: 99%

“…This study builds on the work of Yan et al [6] and Nillesen et al [7] using a combination of maximum cross-correlation values and adaptive mean squares (AMS) filter values as external forces of a gradient-based deformable simplex mesh model. The cross-correlation values were obtained from a semi-3D coarseto-fine displacement algorithm (developed for strain estimation).…”

Section: Introductionmentioning

confidence: 99%

3D Cardiac Segmentation Using Temporal Correlation of Radio Frequency Ultrasound Data

Nillesen¹,

Lopata²,

Huisman³

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. Semi-automatic segmentation of the myocardium in 3D echographic images may substantially support clinical diagnosis of heart disease. Particularly in children with congenital heart disease, segmentation should be based on the echo features solely since a priori knowledge on the shape of the heart cannot be used. Segmentation of echocardiographic images is challenging because of the poor echogenicity contrast between blood and the myocardium in some regions and the inherent speckle noise from randomly backscattered echoes. Phase information present in the radio frequency (rf) ultrasound data might yield useful, additional features in these regions. A semi-3D technique was used to determine maximum temporal cross-correlation values locally from the rf data. To segment the endocardial surface, maximum cross-correlation values were used as additional external force in a deformable model approach and were tested against and combined with adaptive filtered, demodulated rf data. The method was tested on full volume images (Philips, iE33) of four healthy children and evaluated by comparison with contours obtained from manual segmentation.

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Segmentation of the Heart Muscle in 3-D Pediatric Echocardiographic Images

Cited by 32 publications

References 30 publications

Correlation-based discrimination between myocardial tissue and blood in 3D echocardiographic images

Correlation-based discrimination between myocardial tissue and blood in 3D echocardiographic images

Functional cardiovascular ultrasound imaging: Quantification of plaque vulnerability and cardiac function

3D Cardiac Segmentation Using Temporal Correlation of Radio Frequency Ultrasound Data

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