Region competition based active contour for medical object extraction

Shang, Yanfeng; Yang, Xin; Zhu, Lei; Deklerck, Rudi; Nyssen, Edgard

doi:10.1016/j.compmedimag.2007.10.004

Cited by 39 publications

(26 citation statements)

References 20 publications

(30 reference statements)

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“…This is done by first reducing the ultrasound intensity information at the surface nodes to a two-dimensional image, where the axes of the image are the search plane angle and the arc radial offset (Figure 3). The leaflet nodes are then separated from the blood pool nodes using the RCAC level set method [3], where the level set function is initialized using a k-means algorithm. The annulus nodes are always made to be included in the leaflet group during the level set evolution, and the continuity between the first and last search plane is maintained.…”

Section: Trimming An Extended Leaflet Surfacementioning

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: Trimming An Extended Leaflet Surfacementioning

confidence: 99%

“…An example of a volumetric method for 3DUS was shown in [3], which used an intensity-based level set method to locate the leaflets. Unfortunately the method included not only the leaflets but anything connected to the leaflets with similar intensity, such as the left ventricle wall, thus failing to isolate the leaflets.…”

Section: Introductionmentioning

confidence: 99%

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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show abstract

“…The segmentation algorithms proposed in the latter two works produce surface representations of the open valve from 3D US images. Finally, Shang et al 22 demonstrate volumetric rendering of the mitral valve using an intensity-based level set method, but this segmentation algorithm also captures extraneous structures with similar intensities, such as the left ventricular wall, in addition to the mitral leaflets. The objective of this paper is to introduce a novel approach to valve delineation and morphometry that produces volumetric reconstructions of the anterior and posterior leaflets at midsystole in transesophageal 3D US images.…”

Section: Introductionmentioning

confidence: 99%

“…[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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“…Active contour models (ACM) is one of the most successful techniques for object detection in images and have been widely used in image segmentation [1][2][3], analysis of medical images [4], analysis of protein spots in two-dimensional gel electrophoresis images [5], and many other tasks. ACMs are based on an energy minimization approach and allow incorporating a priori knowledge.…”

Section: Introductionmentioning

confidence: 99%

Learning Accurate Active Contours

Gelžinis

Verikas

Bačauskienė

et al. 2013

Engineering Applications of Neural Networks

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Abstract. Focus of research in Active contour models (ACM) area is mainly on development of various energy functions based on physical intuition. In this work, instead of designing a new energy function, we generate a multitude of contour candidates using various values of ACM parameters, assess their quality, and select the most suitable one for an object at hand. A random forest is trained to make contour quality assessments. We demonstrate experimentally superiority of the developed technique over three known algorithms in the P. minimum cells detection task solved via segmentation of phytoplankton images.

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Region competition based active contour for medical object extraction

Cited by 39 publications

References 20 publications

Modeling Mitral Valve Leaflets from Three-Dimensional Ultrasound

Modeling Mitral Valve Leaflets from Three-Dimensional Ultrasound

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

Learning Accurate Active Contours

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