Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models

Boisvert, Jonathan; Cheriet, Foued; Pennec, Xavier; Labelle, Hubert; Ayache, Nicholas

doi:10.1109/tmi.2007.911474

Cited by 70 publications

(44 citation statements)

References 40 publications

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“…In this paper, we propose a method for reconstructing the spine from its midline that uses an articulated model [7] for describing anatomical variability. This model effectively represents vertebrae inter-dependency and it has already demonstrated capabilities for inferring missing information [8].…”

Section: Introductionmentioning

confidence: 99%

Fast 3D Reconstruction of the Spine Using User-Defined Splines and a Statistical Articulated Model

Moura¹,

Boisvert

Barbosa³

et al. 2009

Advances in Visual Computing

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Abstract. This paper proposes a method for rapidly reconstructing 3D models of the spine from two planar radiographs. For performing 3D reconstructions, users only have to identify on each radiograph a spline that represents the spine midline. Then, a statistical articulated model of the spine is deformed until it best fits these splines. The articulated model used on this method not only models vertebrae geometry, but their relative location and orientation as well. The method was tested on 14 radiographic exams of patients for which reconstructions of the spine using a manual identification method where available. Using simulated splines, errors of 2.2±1.3mm were obtained on endplates location, and 4.1±2.1mm on pedicles. Reconstructions by non-expert users show average reconstruction times of 1.5min, and mean errors of 3.4mm for endplates and 4.8mm for pedicles. These results suggest that the proposed method can be used to reconstruct the human spine in 3D when user interactions have to be minimised.

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Section: Introductionmentioning

confidence: 99%

Fast 3D Reconstruction of the Spine Using User-Defined Splines and a Statistical Articulated Model

Moura¹,

Boisvert

Barbosa³

et al. 2009

Advances in Visual Computing

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“…A first interesting example was proposed by Jonathan Boisvert [13,12] with a 3D articulated model of the spine. The model gathers the relative configurations of the vertebrae along the spinal chord (the parameters are the rigid transforms that superpose neighboring vertebrae) rather than the position and orientation of each vertebra in a global reference frame.…”

Section: A Statistical Shape Model Of the Scoliotic Spinementioning

confidence: 99%

Statistical Computing on Non-Linear Spaces for Computational Anatomy

Pennec

Fillard

2015

Handbook of Biomedical Imaging

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Computational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings.

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“…Boisvert et al [1] present a model that describes the statistical variations of the spine in terms of sequential rigid transformations of the local vertebra coordinate systems. Using principal component analysis on the Riemannian manifold of rigid transformations they can extract clinically meaningful eigenmodes.…”

Section: Related Workmentioning

confidence: 99%

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

Kelm

Zhou

Suehling

et al. 2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. Determining spinal geometry and in particular the position and orientation of the intervertebral disks is an integral part of nearly every spinal examination with Computed Tomography (CT) and Magnetic Resonance (MR) imaging. It is particularly important for the standardized alignment of the scan geometry with the spine. In this paper, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the intervertebral disks in a given spinal image volume. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Since the proposed approach is learning-based it can be trained for MR or CT alike. Experimental results based on 42 MR volumes show that our system not only achieves superior accuracy but also is the fastest system of its kind in the literature -on average, the spinal disks of a whole spine are detected in 11.5s with 98.6% sensitivity and 7.3% false positive detections. An average position error of 2.4mm and angular error of 3.9 • is achieved.

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Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models

Cited by 70 publications

References 40 publications

Fast 3D Reconstruction of the Spine Using User-Defined Splines and a Statistical Articulated Model

Fast 3D Reconstruction of the Spine Using User-Defined Splines and a Statistical Articulated Model

Statistical Computing on Non-Linear Spaces for Computational Anatomy

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

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