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Abstract Background The current study’s purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI)). Body composition and bone parameters were evaluated by dual-energy X-ray absorptiometry (DXA). Results Weight, lean mass, body mass index, femoral neck cross-sectional area (FN CSA), FN section modulus (Z), FN cross sectional moment of inertia (CSMI), intertrochanteric (IT) CSA, IT Z, IT CSMI, IT cortical thickness (CT), femoral shaft (FS) CSA, FS Z and FS CSMI were significantly greater (p

Background: The current study's purpose is to compare hip structural analysis variables in a group of postmenopausal women with sarcopenia and another group of postmenopausal women with normal skeletal muscle mass index. To do so, the current study included 8 postmenopausal women (whose ages ranged between 65 and 84 years) with sarcopenia and 60 age-matched controls (with normal skeletal muscle mass index (SMI)). Body composition and bone parameters were evaluated by dual-energy X-ray absorptiometry (DXA). Results: Weight, lean mass, body mass index, femoral neck cross-sectional area (FN CSA), FN section modulus (Z), FN cross sectional moment of inertia (CSMI), intertrochanteric (IT) CSA, IT Z, IT CSMI, IT cortical thickness (CT), femoral shaft (FS) CSA, FS Z and FS CSMI were significantly greater (p < 0.05) in women with normal SMI compared to women with sarcopenia. In the whole population, SMI was positively associated with IT CSA, IT Z, IT CSMI, IT CT, FS CSA, FS Z, FS CSMI, FS CT but negatively correlated to IT buckling ratio (BR) and FS BR. Conclusion: The current study suggests that sarcopenia has a negative effect on hip bone strength indices in postmenopausal women. [ABSTRACT FROM AUTHOR]

Regression-based schemes have proven effective for locating landmarks on images. Most previous approaches either predict the positions of all points simultaneously, or use regressors that predict individual points combined with a global shape constraint. The former can be efficient, but such models tend to be less robust. Conversely, Random Forest (RF) voting methods for individual points have been shown to be robust and accurate, but can lead to very large models. We explore the continuum between these two approaches by training RF regressors to predict subsets of points. Multi-point regression voting was implemented within the Random Forest Regression Voting Constrained Local Model frame- work and evaluated on clinical and facial images. Significant model size reductions were achieved with little difference in accuracy. The approach may therefore be useful where high numbers of points, and limitations on memory or disk space, make single-point models impractically large. [ABSTRACT FROM AUTHOR]