Statistical shape modeling of proximal femoral shape deformities in Legg–Calvé–Perthes disease and slipped capital femoral epiphysis

Abstract

Introduction The current understanding of morphological deformities of the hip such as femoroacetabular impingement (FAI), Legg-Calve-Perthes disease (LCPD), and slipped capital femoral epiphysis (SCFE) is based on 2-dimensional metrics, primarily involving the femoral head, that only partially describe the complex skeletal morphology. Objective This study aimed to improve the 3-dimensional understanding of shape variations during normal growth, and in LCPD and SCFE, through statistical shape modeling. Design Thirty-two patients with asymptomatic, LCPD, and SCFE hips, determined from physical and radiographic examination, were scanned using 3-D CT at a voxel size of (0.5–0.9mm)2 in-plane and 0.63mm slice thickness. Statistical shape modeling was performed on segmented proximal femoral surfaces to determine modes of variation and shape variables quantifying 3-D shape. In addition, conventional variables were determined for all femora. Results Proximal femur shape was described by 8 modes of variation and corresponding shape variables. Statistical shape variables were distinct with age and revealed coordinated, growth-associated differences in neck length-to-width ratio, femoral head medialization, and trochanter protrusion. After size and age-based shape adjustment, diseased proximal femora were characterized by shape variables distinct from those of asymptomatic hips. The shape variables defined morphology in health and disease, and were correlated with certain conventional variables of shape, including neck-shaft angle, head diameter, and neck diameter. Conclusion 3-D quantitative analyses of proximal femoral bone shape during growth and in disease are useful for furthering the understanding of normal and abnormal shape deviations which affect cartilage biomechanics and risk of developing osteoarthritis.

Men and women have similarly shaped carpometacarpal joint bones

“…Previous statistical shape models investigating morphological variation in bones have shown that size dominates the first principal component (Chan et al, 2013; Cootes et al, 1992; Fitzpatrick et al, 2011; Zhang et al, 2014). This was the case in our statistical shape model, where the first principal component accounted for 71% of the morphological variation in CMC joint bones, which was attributed to size (Figure 2).…”

“…For statistical shape models involved in biological systems, size typically dominates the first principal component (Chan et al, 2013; Cootes et al, 1992; Fitzpatrick et al, 2011; Zhang et al, 2014). To investigate pure-shape differences in the trapezia and 1 st metacarpal bones between men and women, a general Procrustes analysis (least-squares minimization of correspondent point differences) was performed on the fitted meshes to filter out scaling variations (Bischoff et al, 2013; Ross, 2004; Stegmann and Gomez, 2002) producing the size-normalized shape model.…”

“…Statistical shape modeling is necessary to quantify the variation in shape across a population (Chan et al, 2013; Fitzpatrick et al, 2011; Van Haver et al, 2013; Zhang et al, 2014) and classify morphological variation by decomposing shapes into a set of statistically significant modes (commonly principal components) (Cootes et al, 1992; Dryden and Mardia, 1998). This is effective for understanding morphology (Bischoff et al, 2013; Vos et al, 2004) and for determining relationships between morphology and parameters of interest, such as sex and age (Anderson et al, 2010; Bischoff et al, 2013; Fitzpatrick et al, 2011).…”

Study of the interactions between proximal femur 3d bone shape, cartilage health, and biomechanics in patients with hip Osteoarthritis

“…While 3D bone shape analysis using MRI has the potential to extract more detailed shape features, challenges such as segmenting the bone and other tissues becomes time intensive if done manually, or even semi-automatically [28–31]. Despite this constraint Three-dimensional Statistical Shape Modeling (SSM) has been increasingly employed in studies involving knee and hip OA [28–32]. …”

3‐dimensional metrics of proximal femoral shape deformities in Legg–Calvé–Perthes disease and slipped capital femoral epiphysis

“…Previously, the authors conducted a study in which a SSM model was created for asymptomatic, LCPD, and SCFE patients. That study showed that statistical shape parameters described coordinated, regional shape deformations that correlated with certain traditional 2-D measurements 26 . One limitation of previous studies is that modes of variation can be difficult to interpret and apply in a clinical setting.…”