A knowledge‐guided active model method of cortical structure segmentation on pediatric MR images

Shan, Zack; Parra, Carlos; Ji, Qing; Jain, Jinesh; Reddick, Wilburn E.

doi:10.1002/jmri.20688

Cited by 8 publications

(4 citation statements)

References 31 publications

(27 reference statements)

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“…SSM has previously been applied to image processing tasks such as image segmentation, registration, object recognition, and diagnosis (Babalola et al, 2006; Benameur et al, 2005; Dornaika and Ahlberg, 2006; Ferrarini et al, 2006; Koikkalainen et al, 2007; Rueckert et al, 2003; Shan et al, 2006), and more recently extended and applied to investigating skeletal fracture risk (Bredbenner and Nicolella, 2007a, 2007b, 2008). SSM reduces the shape dimensionality of the object of interest from a large set of highly correlated variables (typically a set of surface vertices) to a compact set of independent and uncorrelated variables.…”

Section: Introductionmentioning

confidence: 99%

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Bredbenner

Eliason

Potter

et al. 2010

Journal of Biomechanics

126

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We hypothesize that variability in knee subchondral bone surface geometry will differentiate between patients at risk and those not at risk for developing osteoarthritis (OA) and suggest that statistical shape modeling (SSM) methods form the basis for developing a diagnostic tool for predicting the onset of OA. Using a subset of clinical knee MRI data from the osteoarthritis initiative (OAI), the objectives of this study were to (1) utilize SSM to compactly and efficiently describe variability in knee subchondral bone surface geometry and (2) determine the efficacy of SSM and rigid body transformations to distinguish between patients who are not expected to develop osteoarthritis (i.e. Control group) and those with clinical risk factors for OA (i.e. Incidence group). Quantitative differences in femur and tibia surface geometry were demonstrated between groups, although differences in knee joint alignment measures were not statistically significant, suggesting that variability in individual bone geometry may play a greater role in determining joint space geometry and mechanics. SSM provides a means of explicitly describing complete articular surface geometry and allows the complex spatial variation in joint surface geometry and joint congruence between healthy subjects and those with clinical risk of developing or existing signs of OA to be statistically demonstrated.

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Section: Introductionmentioning

confidence: 99%

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Bredbenner

Eliason

Potter

et al. 2010

Journal of Biomechanics

126

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“…Statistical shape models, introduced over ten years ago in the image processing community [66], have been successfully used to simplify image processing tasks such as image segmentation, registration, object recognition, and diagnosis [67][68][69][70][71][72][73]. The basic principle is to reduce the shape dimensionality of the object of interest from a large set of highly correlated variables (typically a large set of surface vertices) to a compact set of independent and uncorrelated variables.…”

Section: Statistical Shape Modeling Efficiently and Compactly Describmentioning

confidence: 99%

Computational Analysis of Bone Fracture

Nicolella

Bredbenner

2014

Accidental Injury

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Age-related non-traumatic fractures are a major public health problem with fracture of the proximal femur resulting in significant patient mortality. Currently, the clinical gold standard for assessing an individual's fracture risk is dual-energy x-ray absorptiometry (DEXA). Although DXA-based bone mineral density (BMD) measurements have been shown to correlate with fracture risk, BMD distributions describing normal individuals and those who have suffered a hip fracture contain significant overlap, thereby reducing the specificity of BMD based fracture risk categorization. Since bone strength results from a combination of bone mass, bone micro-architecture and material properties, and overall bone geometry, two-dimensional imaging based diagnostic approaches such as DEXA are limited in their ability to specifically predict bone strength. Engineering models of skeletal structures that combine descriptions of three dimensional bone geometry, bone mass distribution, and bone material behavior into a high fidelity simulation have been shown to predict bone strength with an improved accuracy compared to DEXA alone, albeit with some limitations. Specifically, voxel based finite element models derived from QCT image data are based on correlations between predicted structural stiffness and structural failure and therefore are generally only valid for simple uniaxial loading; it is unlikely this approach would be valid for more complex applied loads. Furthermore, these models generally use simplified bone material descriptions that do not capture the demonstrated non-linear damaging behavior of bone and do not model bone material

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“…Fortunately, new approaches have enabled partial automation of FE model generation through the use of statistical deformable models. Among other applications, statistical deformable models have allowed automation of medical image segmentation (Shan et al 2006), shape recognition (Dornaika and Ahlberg 2006), and disease diagnosis (Bredbenner et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Analysis of temporomandibular joint dysfunction in paediatric patients with unilateral crossbite using automatically generated finite element models

Ortún‐Terrazas

Cegoñino

Illipronti-Filho

et al. 2020

Computer Methods in Biomechanics and Biomedical Engineering

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A knowledge‐guided active model method of cortical structure segmentation on pediatric MR images

Cited by 8 publications

References 31 publications

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the Osteoarthritis Initiative database

Computational Analysis of Bone Fracture

Analysis of temporomandibular joint dysfunction in paediatric patients with unilateral crossbite using automatically generated finite element models

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