Three Dimensional Reconstruction and Dynamic Analysis of Mitral Annular Based on Connected Equi-length Curve Angle Chain

Zhu, Lei; Yang, Xin; Yao, Liping; Kim, Sun

doi:10.1007/978-3-540-77413-6_38

Cited by 4 publications

(6 citation statements)

References 16 publications

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“…One reason is that the usual practice is to pick points in 2D slices taken from a 3D volume, so the user only has access to a portion of the available information at any given step, forcing the user to mentally interpolate 3D information. The methods presented in [23] and [24] attempt to correct for the deficiencies in these manual slice-based segmentations, but do so by smoothing the segmentations without referring back to the original 3D data from which they were made.…”

Section: Introductionmentioning

confidence: 99%

Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts

Schneider¹,

Perrin

Vasilyev

et al. 2010

IEEE Trans. Med. Imaging

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The shape of the mitral valve annulus is used in diagnostic and modeling applications, yet methods to accurately and reproducibly delineate the annulus are limited. This paper presents a mitral annulus segmentation algorithm designed for closed mitral valves which locates the annulus in three-dimensional ultrasound using only a single user-specified point near the center of the valve. The algorithm first constructs a surface at the location of the thin leaflets, and then locates the annulus by finding where the thin leaflet tissue meets the thicker heart wall. The algorithm iterates until convergence metrics are satisfied, resulting in an operator-independent mitral annulus segmentation. The accuracy of the algorithm was assessed from both a diagnostic and surgical standpoint by comparing the algorithm’s results to delineations made by a group of experts on clinical ultrasound images of the mitral valve, and to delineations made by an expert with a surgical view of the mitral annulus on excised porcine hearts using an electromagnetically tracked pointer. In the former study, the algorithm was statistically indistinguishable from the best performing expert (p = 0.85) and had an average RMS difference of 1.81 ± 0.78mm to the expert average. In the latter, the average RMS difference between the algorithm’s annulus and the electromagnetically tracked points across six hearts was 1.19 ± 0.17mm.

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Section: Introductionmentioning

confidence: 99%

Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts

Schneider¹,

Perrin

Vasilyev

et al. 2010

IEEE Trans. Med. Imaging

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“…Most studies of annular shape rely on expert manual segmentation [1], [3], [4], [6], [8], [10], [16] (mitral), [2], [7], [15] (tricuspid). A few methods for semi-automatic segmentation of valve annulae have been proposed in the last decade, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding imaging modalities, echocardiography has been historically preferred in the literature for annulus segmentation, even in recent publications [16], [18], as it has good temporal resolution, is safe, cheap and non-invasive. However, if motion analysis is sacrificed, three-dimensional (3D) Magnetic Resonance Imaging (MRI) provides much better image quality than echocardiography, principally because of higher spatial resolution and lack of speckle texture.…”

Section: Introductionmentioning

confidence: 99%

Cardiac valve annulus manual segmentation using computer assisted visual feedback in three-dimensional image data

Casero

Burton

Quinn

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Annulus manual segmentation is an important tool for the study of valve anatomy and physiology, for the four main valves of the heart (mitral, tricuspid, aortic and pulmonary). In this paper we review two traditional manual segmentation approaches: slice-by-slice and interpolating a sparse set of landmarks with a spline curve. We propose a new Spline Tool for the open source software platform Seg3D, that is fast and improves spatial coherence by providing visual feedback of the segmentation in real time. The Spline Tool was tested successfully on 14 rat hearts, on all four valves.

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“…Current methods to segment the annulus are limited, requiring many researchers and clinicians to resort to manual segmentation of 2D slices [5]. While these segmentations can be accurate, they are time consuming and only allow access to a portion of the available information at each step.…”

Section: Introductionmentioning

confidence: 99%

“…Some methods attempt to correct for the sparse and discontinuous slice-based manual segmentations, but rely solely on the segmented points and do not refer back to the original data (e.g. [5,6]). A semi-automatic segmentation algorithm is presented in [7], however, the method again relies strongly on 2D information and does not appear to take advantage of three-dimensional data – neither from the surroundings nor its own segmentation – while delineating the annulus.…”

Section: Introductionmentioning

confidence: 99%

Mitral annulus segmentation from three-dimensional ultrasound

Schneider¹,

Perrin²,

Vasilyev

et al. 2009

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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An accurate and reproducible segmentation of the mitral valve annulus from 3D ultrasound is useful to clinicians and researchers in applications such as pathology diagnosis and mitral valve modeling. Current segmentation methods, however, are based on 2D information, resulting in inaccuracies and a lack of spatial coherence. We present a segmentation algorithm which, given a single user-specified point near the center of the valve, uses max-flow and active contour methods to delineate the annulus geometry in 3D. Preliminary comparisons to manual segmentations and a sensitivity study show the algorithm is both accurate and robust.

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Three Dimensional Reconstruction and Dynamic Analysis of Mitral Annular Based on Connected Equi-length Curve Angle Chain

Cited by 4 publications

References 16 publications

Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts

Mitral Annulus Segmentation From 3D Ultrasound Using Graph Cuts

Cardiac valve annulus manual segmentation using computer assisted visual feedback in three-dimensional image data

Mitral annulus segmentation from three-dimensional ultrasound

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