A MRF Based Random Graph Modelling the Human Cortical Topography

Mangin, Jean‐François; Régis, Jean; Bloch, Isabelle; Frouin, Vincent; Samson, Y.; López-Krahe, Jaime

doi:10.1007/978-3-540-49197-2_20

Cited by 17 publications

(8 citation statements)

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“…This paper does not aim at providing a detailed description of the large battery of algorithms which is used to define sulci and gyri. Most of these algorithms, indeed, have been described previously elsewhere [12], [46]- [48], [51], [52], [62]. The goal of this paper is to provide an overview of the whole system, before focusing on the first meaningful neuroscience result obtained within this framework.…”

Section: Introductionmentioning

confidence: 96%

Object-Based Morphometry of the Cerebral Cortex

Mangin

Rivière

Cachia

et al. 2004

IEEE Trans. Med. Imaging

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140

130

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Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.

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Section: Introductionmentioning

confidence: 96%

Object-Based Morphometry of the Cerebral Cortex

Mangin

Rivière

Cachia

et al. 2004

IEEE Trans. Med. Imaging

Self Cite

140

130

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show abstract

“…We do not focus on a fully automatic recognition algorithm (see for that [22], [25], [31]) as a common standard nomenclature for sulcal topography is not fully defined yet. In our opinion, the problem resides more in the definition of a way to present objective information to help the user (anatomist, neurosurgeon, etc., ) in the labeling task than in a fully automatic process which will be valid only for the main sulci.…”

Section: Introductionmentioning

confidence: 99%

Automated extraction and variability analysis of sulcal neuroanatomy

Goualher

Procyk

Collins

et al. 1999

IEEE Trans. Med. Imaging

150

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Systematic mapping of the variability in cortical sulcal anatomy is an area of increasing interest which presents numerous methodological challenges. To address these issues, we have implemented sulcal extraction and assisted labeling (SEAL) to automatically extract the two-dimensional (2-D) surface ribbons that represent the median axis of cerebral sulci and to neuroanatomically label these entities. To encode the extracted three-dimensional (3-D) cortical sulcal schematic topography (CSST) we define a relational graph structure composed of two main features: vertices (representing sulci) and arcs (representing the relationships between sulci). Vertices contain a parametric representation of the surface ribbon buried within the sulcus. Points on this surface are expressed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). For each of these vertices, we store length, depth, and orientation as well as anatomical attributes (e.g., hemisphere, lobe, sulcus type, etc.). Each arc stores the 3-D location of the junction between sulci as well as a list of its connecting sulci. Sulcal labeling is performed semiautomatically by selecting a sulcal entity in the CSST and selecting from a menu of candidate sulcus names. In order to help the user in the labeling task, the menu is restricted to the most likely candidates by using priors for the expected sulcal spatial distribution. These priors, i.e., sulcal probabilistic maps, were created from the spatial distribution of 34 sulci traced manually on 36 different subjects. Given these spatial probability maps, the user is provided with the likelihood that the selected entity belongs to a particular sulcus. The cortical structure representation obtained by SEAL is suitable to extract statistical information about both the spatial and the structural composition of the cerebral cortical topography. This methodology allows for the iterative construction of a successively more complete statistical models of the cerebral topography containing spatial distributions of the most important structures, their morphometrics, and their structural components.

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“…Related to our work is the work in [15], which studies the cortical topography by determining a graph of the sulci. Also, related is the work in [19, 181, which determines the medial axis of arbitrary shapes at multiple scales.…”

Section: Introductionmentioning

confidence: 99%

Finding 3D parametric representations of the deep cortical folds

Vaillant

Davatzikos

Bryan

1996

Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis

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Parametric representations of anatomical structures provide useful mathematical descriptions for m a n y medical imaging applications, including morphological analysis of the brain. In this paper, we develop a methodology f o r obtaining a parametric representation of the deep cortical folds of the brain utilizing characteristics of the cortical shape. W e first find a mathematical representation of the outer cortical surface using a deformable surface algorithm. Using the principal curvatures of the resulting surface, we then identify the edges o n the sulci o n it, and we initialize active contours along them. An external force field guides a n active contour t o the deep edge of a sulcus, along the medial surface of a cortical fold. A parametric description of a sulcal surface is obtained as the active contour traverses the sulcus, sweeping a surface resembling a convoluted ribbon embedded in 30. I n this paper we present results using magnetic resonance images.

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A MRF Based Random Graph Modelling the Human Cortical Topography

Cited by 17 publications

References 1 publication

Object-Based Morphometry of the Cerebral Cortex

Object-Based Morphometry of the Cerebral Cortex

Automated extraction and variability analysis of sulcal neuroanatomy

Finding 3D parametric representations of the deep cortical folds

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