A statistical model of shape and density of the proximal femur in relation to radiological and clinical OA of the hip

Waarsing, J.H.; Rozendaal, Rianne M; Verhaar, Jan A N; Bierma-Zeinstra, Sita M A; Weinans, Harrie

doi:10.1016/j.joca.2010.02.003

Cited by 57 publications

(57 citation statements)

References 33 publications

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“…In a study of the proximal femur, Gregory et al (2007) reported that flatter femoral heads and shorter femoral necks are associated with OA. However, conflicting results are reported in other works (Lynch et al 2009;Waarsing et al 2010). In terms of fracture risk, it is understood that localised changes in femoral neck and trochanter morphology can have significant effects on fracture resistance (Yang et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Shape is routinely characterised using statistical shape models (SSMs), for which a training set of shapes is described in a correspondent manner, then decomposed into a mean shape and significant components of variation. The use of SSM for femurs can be categorised based on application: image segmentation (Ramme et al 2011); model reconstruction (Rajamani et al 2007; Barratt et al 2008;Baka et al 2011); finite element modelling (Bryan et al 2010) and pathology classification and prediction (Gregory et al 2007;Lynch et al 2009;Waarsing et al 2010). An exhaustive review of these works is beyond the scope of this article, but to the best of the authors' knowledge, all treat the femur (or the distal or proximal femur when focusing on the knee or hip joint) as a single object in the SSM.…”

Section: Introductionmentioning

confidence: 99%

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An anatomical region-based statistical shape model of the human femur

Malcolm

Hislop-Jambrich³

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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We present a workflow for producing a statistical shape model (SSM) of the femur with automatically defined regions resembling general anatomic features. Explicitly defined regions enforce correspondence of anatomical features, and allow the shapes of regions to be analysed independently if needed. A training set of manually segmented femur surfaces are partitioned according to Gaussian curvature. Partitioned regions across the training set are then grouped using mean-shift clustering to identify the most stable regions into which surfaces are divided. Reference piecewise parametric meshes are designed for and fitted to each region, and used to train regional SSMs through fitting -training iterations. Fitted region meshes are assembled into full femur meshes for training a whole femur region-based SSM (rSSM). Partitioning, clustering and shape modelling results are presented for 41 femurs. In comparison to a non-regional SSM, the rSSM was more efficient and correspondent in its approximation of unseen femurs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An anatomical region-based statistical shape model of the human femur

Malcolm

Hislop-Jambrich³

et al. 2014

Computer Methods in Biomechanics and Biomedical Engineering: Im

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“…Beyond shape, population models can include internal femur architecture [17,20], correlations to fracture and disease risk [14,21], mechanical behaviour [22] and anthropological and anthropometric data [5]. These models allow the prediction of femoral structure from correlated data, or vice versa.…”

Section: Population Models and The Musculoskeletal Atlas Projectmentioning

confidence: 99%

Multiscale musculoskeletal modelling, data–model fusion and electromyography-informed modelling

Fernandez

Heidlauf

et al. 2016

Interface Focus.

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This paper proposes methods and technologies that advance the state of the art for modelling the musculoskeletal system across the spatial and temporal scales; and storing these using efficient ontologies and tools. We present population-based modelling as an efficient method to rapidly generate individual morphology from only a few measurements and to learn from the ever-increasing supply of imaging data available. We present multiscale methods for continuum muscle and bone models; and efficient mechanostatistical methods, both continuum and particle-based, to bridge the scales. Finally, we examine both the importance that muscles play in bone remodelling stimuli and the latest muscle force prediction methods that use electromyography-assisted modelling techniques to compute musculoskeletal forces that best reflect the underlying neuromuscular activity. Our proposal is that, in order to have a clinically relevant virtual physiological human, (i) bone and muscle mechanics must be considered together; (ii) models should be trained on population data to permit rapid generation and use underlying principal modes that describe both muscle patterns and morphology; and (iii) these tools need to be available in an open-source repository so that the scientific community may use, personalize and contribute to the database of models.

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“…Previous studies have proposed that the outcome parameters from active shape and appearance models (AAM) (Cootes et al, 2001), called modes, may include information on the rotational alignment of the femur in a 2D image (Waarsing et al, 2010). If this hypothesis is true, the modes could be used to predict the rotation of the femur.…”

Section: Introductionmentioning

confidence: 99%

“…If this hypothesis is true, the modes could be used to predict the rotation of the femur. Two-dimensional active appearance models have also been used for evaluation of the relation between osteoarthritis in hip and femoral shape deformations (Waarsing et al, 2010). However, the outcome of the 2D AAM may be affected by the misalignment of the femurs in the X-rays used to create the AAM.…”

Section: Introductionmentioning

confidence: 99%