Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts

Schneider, Robert J.; Perrin, Douglas P.; Vasilyev, Nikolay V.; Marx, Gerald R.; Nido, Pedro J. del; Howe, Robert D.

doi:10.1109/tmi.2010.2050595

Cited by 61 publications

(72 citation statements)

References 30 publications

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“…Accurately locating the annulus is therefore important as its location directly affects the leaflet model. To find the annulus, we first use our annulus segmentation algorithm to find the annulus in a frame with a closed valve [6]. We then track that annulus to all other frames using a modified version of the Lucas & Kanade optical flow method [7].…”

Section: Discussionmentioning

confidence: 99%

“…The edges of the graph have a capacity that is a function of a drive image, which is computed at each point. The drive image on the first pass is a product of the original 3DUS intensity (I) and a thin tissue detector (TTD) [6], I drv = (I) (TTD). For the final pass, because we have an estimate for the leaflet location, a weighting function, W, is incorporated into the drive image, I drv = (I) (TTD) (W), to encourage the min-cut to be found at the estimated leaflet location.…”

Section: Estimating An Extended Leaflet Surfacementioning

confidence: 99%

“…A frame showing an open valve was manually selected from each of the four cases. The annulus, which acted as the input to the algorithm, was found using our semi-automatic annulus segmentation method [6] and a tracking algorithm based on the Lucas & Kanade method [7]. Using the location of the tracked annulus in the manually selected frame with an open valve, the leaflets were then found using the presented method.…”

Section: Performance and Validationmentioning

confidence: 99%

“…The method is designed for open mitral valves because we want to differentiate between anterior and posterior leaflets, and making this distinction for coapted leaflets in 3DUS is extremely difficult even for a human observer. After finding the location of the annulus using our previously developed semi-automatic algorithm [6] and an automated optical flow tracking method based on the work by Lucas & Kanade [7], the leaflet modeling method automatically finds the location and extent of the mitral leaflets and generates a mesh to represent the geometry with no additional user interaction. We chose to represent the leaflets as a mesh because this representation can be easily used to generate either a volumetric or parametric representation if needed, is a suitable input for mechanical modeling, and is representative of the the thin leaflet tissue.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Mitral Valve Leaflets from Three-Dimensional Ultrasound

Schneider

Burke

Marx

et al. 2011

Functional Imaging and Modeling of the Heart

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Abstract. The geometry of the mitral leaflets, most commonly viewed using three-dimensional ultrasound, is an important input for mechanical models predicting valve closure. Current methods for leaflet modeling from ultrasound either require extensive user interaction, rely on inaccurate assumptions, or produce generic results. The presented method for modeling the mitral leaflets from three-dimensional ultrasound of an open mitral valve is automatic and able to capture patient specific geometry. The method requires as an input the location of the mitral annulus, which we generate semi-automatically using our previously designed method. No additional user interaction is required. The leaflet modeling algorithm operates by first constructing an extended surface at the location of the leaflets which extends beyond the leaflet edges into the blood pool, and then trims the surface to the observed length of the leaflets. Preliminary results suggest the leaflet modeling method, combined with our previous method for annulus segmentation, generates accurate patient-specific mitral valve models.

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Section: Discussionmentioning

confidence: 99%

Section: Estimating An Extended Leaflet Surfacementioning

confidence: 99%

Section: Performance and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Mitral Valve Leaflets from Three-Dimensional Ultrasound

Schneider

Burke

Marx

et al. 2011

Functional Imaging and Modeling of the Heart

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“…[13][14][15] Several studies have explored automated ultrasound image analysis of the mitral valve, [16][17][18][19][20][21][22] including tracking of the anterior leaflet in 2D ultrasound images and segmentation and tracking of the mitral annulus with 3D ultrasound. The recent work of Ionasec et al 18 models and quantifies aortic and mitral valve dynamics using a nonrigid landmark motion model.…”

Section: Introductionmentioning

confidence: 99%

Development of a semi-automated method for mitral valve modeling with medial axis representation using 3D ultrasound

Pouch¹,

Yushkevich²,

Jackson³

et al. 2012

Med. Phys.

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Purpose: Precise 3D modeling of the mitral valve has the potential to improve our understanding of valve morphology, particularly in the setting of mitral regurgitation (MR). Toward this goal, the authors have developed a user-initialized algorithm for reconstructing valve geometry from transesophageal 3D ultrasound (3D US) image data. Methods: Semi-automated image analysis was performed on transesophageal 3D US images obtained from 14 subjects with MR ranging from trace to severe. Image analysis of the mitral valve at midsystole had two stages: user-initialized segmentation and 3D deformable modeling with continuous medial representation (cm-rep). Semi-automated segmentation began with useridentification of valve location in 2D projection images generated from 3D US data. The mitral leaflets were then automatically segmented in 3D using the level set method. Second, a bileaflet deformable medial model was fitted to the binary valve segmentation by Bayesian optimization. The resulting cm-rep provided a visual reconstruction of the mitral valve, from which localized measurements of valve morphology were automatically derived. The features extracted from the fitted cm-rep included annular area, annular circumference, annular height, intercommissural width, septolateral length, total tenting volume, and percent anterior tenting volume. These measurements were compared to those obtained by expert manual tracing. Regurgitant orifice area (ROA) measurements were compared to qualitative assessments of MR severity. The accuracy of valve shape representation with cm-rep was evaluated in terms of the Dice overlap between the fitted cm-rep and its target segmentation. Results: The morphological features and anatomic ROA derived from semi-automated image analysis were consistent with manual tracing of 3D US image data and with qualitative assessments of MR severity made on clinical radiology. The fitted cm-reps accurately captured valve shape and demonstrated patient-specific differences in valve morphology among subjects with varying degrees of MR severity. Minimal variation in the Dice overlap and morphological measurements was observed when different cm-rep templates were used to initialize model fitting. Conclusions: This study demonstrates the use of deformable medial modeling for semi-automated 3D reconstruction of mitral valve geometry using transesophageal 3D US. The proposed algorithm provides a parametric geometrical representation of the mitral leaflets, which can be used to evaluate valve morphology in clinical ultrasound images.

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