2012
DOI: 10.1016/j.media.2012.04.004
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Statistical model based shape prediction from a combination of direct observations and various surrogates: Application to orthopaedic research

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Cited by 49 publications
(26 citation statements)
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“…These points were assumed in the centre of the femur head and the centre of the tibia plafond to properly simulate the shift of mechanical axis during varus/valgus rotation. The position of the RPs was calculated on the basis of bone length, which was estimated with respect to donor height and statistical data [18]. …”
Section: Methodsmentioning
confidence: 99%
“…These points were assumed in the centre of the femur head and the centre of the tibia plafond to properly simulate the shift of mechanical axis during varus/valgus rotation. The position of the RPs was calculated on the basis of bone length, which was estimated with respect to donor height and statistical data [18]. …”
Section: Methodsmentioning
confidence: 99%
“…With faster computers and more advanced image processing softwares, more representative CT or MRI-based musculoskeletal models (Sigal et al, 2009;Baldwin et al, 2010;Vahdati et al, 2014;Martelli et al, 2014;Simpleware Ltd., 2014) can be now readily generated for analysis and micromotions along the entire implant surface can be predicted (Pancanti et al, 2003;Andreaus et al, 2009;Park et al, 2009;Pettersen et al, 2009;Bah et al, 2011;Reimeringer et al, 2012;Fitzpatrick et al, 2014). In this respect, novel pre-clinical evaluation tools have been developed to: (a) enable the variability in patient geometry and bone quality using statistical shape modelling (Bryan et al, 2010;Blanc et al, 2012;Bah et al, 2013;Blanc et al, 2012;Rao et al, 2013;Fitzpatrick et al, 2014); (b) automatically assess the effects of implant positioning, loading, or bone-implant interface conditions for a specific patient (Abdul-Kadir et al, 2008;Bah et al, 2009;Dopico-González et al, 2010) or (c) evaluate and compare the robustness of existing implant designs (Sakai et al, 2008;Reimeringer et al, 2012;Fitzpatrick et al, 2014).…”
Section: Introductionmentioning
confidence: 99%
“…Recently, it was demonstrated that facial shape and appearance are correlated [18] and those correlations were investigated using Canonical Correlation Analysis on separate shape and appearance PCA models. Attributes like age, weight, height, gender are often added to the PCA models as additional linear vectors [15] or with limitations to Gaussian marginal distributions [2].…”
Section: Combined Shape Color and Attribute Modelmentioning
confidence: 99%