Signed Distance Computation Using the Angle Weighted Pseudonormal

Bærentzen, Jakob Andreas; Aanæs, Henrik

doi:10.1109/tvcg.2005.49

Cited by 171 publications

(109 citation statements)

References 23 publications

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“…It should be noticed that overlapping contour regions are defined as negative distances. The reader is kindly directed to (Baerentzen and Aanaes, 2005) for further details on signed distance function computation. A schematic about the competition workflow can be found in Figure 3.…”

Section: Two Contoursmentioning

confidence: 99%

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Morais

Vilaça

Queirós

et al. 2017

Medical Image Analysis

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A competitive strategy for atrial and aortic tract segmentation based on deformable models AbstractMultiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors. Independent functions are used to define each region, being afterward combined to compete for the optimal boundary. The competition locally constrains the surface evolution, prevents overlaps and allows refinement to the walls. Three different scenarios were used to demonstrate the advantages of the proposed approach, through the evaluation of its segmentation accuracy, and its performance for heterogeneous mid walls. Both computed tomography and magnetic resonance imaging datasets were used, presenting results similar to the state-of-the-art methods for both atria and aorta. The competitive strategy showed its superior performance with statistically significant differences against the traditional freeevolution approach in cases with bad image quality or missed atrial/aortic walls. Moreover, only the competitive approach was able to accurately segment the atrial/aortic wall. Overall, the proposed strategy showed to be suitable for atrial region segmentation with a correct segmentation of the mid thin walls, demonstrating its added value with respect to the traditional techniques.

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Section: Two Contoursmentioning

confidence: 99%

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Morais

Vilaça

Queirós

et al. 2017

Medical Image Analysis

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“…To that purpose, we first computed a 3D distance map [4] from any point in the tissue to the epicardium and endocardium, see Figure 1. This distance and its corresponding gradient were then employed to compute the fiber elevation across the thickness.…”

Section: Anatomical Modelmentioning

confidence: 99%

Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

Chabiniok

Moireau

Lesault

et al. 2011

Biomech Model Mechanobiol

101

129

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“…For example, could have the form 12 We use the angle-weighted pseudonormal algorithm [62] to compute the signed distance of the tessellation. The voxel grid is partitioned into inside negative values and outside positive values where and are the two principal curvatures of the surface, computed at vertex .…”

Section: Fitness and Likelihood Functionsmentioning

confidence: 99%

Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops

Ségonne

Pacheco

Fischl

2007

IEEE Trans. Med. Imaging

921

653

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Abstract-In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator.Index Terms-Homotopy, human cerebral cortex, nonseparating loop, Reeb graph, segmentation, topology. I. THE CORTICAL RECONSTRUCTION PROBLEMT HE human cerebral cortex is a highly folded ribbon of gray matter (GM) that lies inside the cerebrospinal fluid (CSF) and outside the white matter (WM) of the brain. Locally, its intrinsic "unfolded" structure constitutes a 2-D sheet, which is several millimeters thick. In the absence of pathology and assuming that the midline hemispheric connections are artificially closed, each cortical hemisphere can be viewed as a simply connected 2-D sheet of neurons that carries the simple topology 1 of a sphere 2 [ Fig. 1 2 The true topology of the gray/white surface is not one of a sphere, as a result of the midline connections between the left and right hemisphere, such as the anterior and the posterior commisures. Fig. 1. (a) The human cerebral cortex is a highly folded ribbon of GM that lies inside the cerebrospinal build and outside the WM of the brain. The green surface represents the interface between WM and GM, and the red surface (i.e., the pial surface) models the interface between GM and CSF. When the midline connections between the left and right hemisphere are artificially closed, these two surfaces have the topology of a sphere. (b) Three-dimensional rendering of the highly folded pial surface. Opposite regions of a sulcus are often contiguous. (c) As a result of the partial volume effect, it is difficult to distinguish opposite banks of the GM from one another. (d) Segmentation algorithms that do not constrain the topology often create cortical segmentations with certain topological defects (i.e., handles). Close-up of a topologically incorrect gray/white surface representation.Recently, there has been a research focus on the extraction of accurate and topologically correct models of the brain surface. Many recent segmentation algorithms for neuroimaging data are able to identify and precisely locate diverse brain structures, although typically without ensuring the validity of the final topology (i.e., that of a sphere). Magnetic resonance images (MRIs) often contain various artifacts (e.g., image noise, image intensity inhomogeneity or nonunifo...

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Signed Distance Computation Using the Angle Weighted Pseudonormal

Cited by 171 publications

References 23 publications

A competitive strategy for atrial and aortic tract segmentation based on deformable models

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops

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