Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data

Gerig, Guido; Koller, Thomas; Székely, Gábor; Brechbühler, Christian; Kübler, Olaf

doi:10.1007/bfb0013783

Cited by 80 publications

(36 citation statements)

References 17 publications

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“…In 1993 Dr. Gerig presented [9] an intra-cranial vessel segmentation technique and illustrated the reduction of those segmentations to graphs that represent their topology. Such branch-based graphs are the basis of much of the vessel-based liver lobe and heart vessel segmentation work being conducted in a variety of labs [8] [14].…”

Section: Introductionmentioning

confidence: 99%

Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination

Aylward¹,

Jomier²,

Vivert³

et al. 2005

Lecture Notes in Computer Science

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Abstract. Graph methods that summarize vasculature by its branching topology are not sufficient for the statistical characterization of a population of intra-cranial vascular networks. Intra-cranial vascular networks are typified by topological variations and long, wandering paths between branch points.We present a graph-based representation, called spatial graphs, that captures both the branching patterns and the spatial locations of vascular networks. Furthermore, we present companion methods that allow spatial graphs to (1) statistically characterize populations of vascular networks, (2) generate the central vascular network of a population of vascular networks, and (3) distinguish between populations of vascular networks. We evaluate spatial graphs by using them to distinguish the gender and handedness of individuals based on their intra-cranial vascular networks.

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Section: Introductionmentioning

confidence: 99%

Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination

Aylward¹,

Jomier²,

Vivert³

et al. 2005

Lecture Notes in Computer Science

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show abstract

“…Graphbased atlases The extraction of a graph modelling the structure of a vascu lar network (i.e., assigning an edge to each vessel branch, and a node to each junc tion/bifurcation) has been a purpose frequently considered by the first vessel segmen tation methods devoted to 3D angiographic data [37,115]. Note that the main weakness of these first approaches was propagation of segmentation errors in the obtained model.…”

Section: Graphbased and Geometric Atlasesmentioning

confidence: 99%

Angiographic Image Analysis

Tankyevych

Talbot

Passat

et al. 2011

Medical Image Processing

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“…For example, endothelial cells play an important part in defining the capillary surface. Visualization of this mutual relationship is difficult using simplified representations of vasculature commonly used for macroscopic data [8,17,4]. …”

Section: Motivationmentioning

confidence: 99%

“…Several surface construction methods discussed previously (Section 3.1) are designed for visualization. A large body of work is also available for estimating vascular surfaces [8,17,4] for visualization. However, these techniques do not capture surface features.…”

Section: Previous Workmentioning

confidence: 99%

Visualization of Cellular and Microvascular Relationships

Mayerich

Abbott

Keyser

2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Understanding the structure of microvasculature structures and their relationship to cells in biological tissue is an important and complex problem. Brain microvasculature in particular is known to play an important role in chronic diseases. However, these networks are only visible at the microscopic level and can span large volumes of tissue. Due to recent advances in microscopy, large volumes of data can be imaged at the resolution necessary to reconstruct these structures. Due to the dense and complex nature of microscopy data sets, it is important to limit the amount of information displayed. In this paper, we describe methods for encoding the unique structure of microvascular data, allowing researchers to selectively explore microvascular anatomy. We also identify the queries most useful to researchers studying microvascular and cellular relationships. By associating cellular structures with our microvascular framework, we allow researchers to explore interesting anatomical relationships in dense and complex data sets.

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Symbolic description of 3-D structures applied to cerebral vessel tree obtained from MR angiography volume data

Cited by 80 publications

References 17 publications

Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination

Spatial Graphs for Intra-cranial Vascular Network Characterization, Generation, and Discrimination

Angiographic Image Analysis

Visualization of Cellular and Microvascular Relationships

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