Statistical Shape Modeling of Skeletal Anatomy for Sex Discrimination: Their Training Size, Sexual Dimorphism, and Asymmetry

Abstract

Purpose: Statistical shape modeling provides a powerful tool for describing and analyzing human anatomy. By linearly combining the variance of the shape of a population of a given anatomical entity, statistical shape models (SSMs) identify its main modes of variation and may approximate the total variance of that population to a selected threshold, while reducing its dimensionality. Even though SSMs have been used for over two decades, they lack in characterization of their goodness of prediction, in particular when defining whether these models are actually representative for a given population.Methods: The current paper presents, to the authors' knowledge, the most extent lower limb anatomy shape model considering the pelvis, femur, patella, tibia, fibula, talus, and calcaneum to date. The present study includes the segmented training shapes (n = 542) obtained from 271 lower limb CT scans. The different models were evaluated in terms of accuracy, compactness, generalizability as well as specificity.Results: The size of training samples needed in each model so that it can be considered population covering was estimated to approximate around 200 samples, based on the generalizability properties of the different models. Simultaneously differences in gender and patterns in left-right asymmetry were identified and characterized. Size was found to be the most pronounced sexual discriminator whereas intra-individual variations in asymmetry were most pronounced at the insertion site of muscles.Conclusion: For models aimed at population covering descriptive studies, the number of training samples required should amount a sizeable 200 samples. The geometric morphometric method for sex discrimination scored excellent, however, it did not largely outperformed traditional methods based on discrete measures.

Mechanics of Psoas Tendon Snapping. A Virtual Population Study

“…Given the profound dominance of size in shape models of human anatomy in the Euclidian subspace (Audenaert, 2019), the tendon velocity-shape relationship was evaluated by means of canonical correlation analysis (CCA) in the Mahalanobis subspace. In particular, the PC values of the shape data, serving as predictor variables for the observed peak tendonvelocity measures, were used.…”

“…While simulation of tendon-muscle positions using finite or discrete element analysis has been shown to be reliable on a patient specific basis, replication of such analyses across large populations introduces an additional ethical, logistical, and computational challenge in order to obtain the required data set (Audenaert et al, 2019b(Audenaert et al, ,c, 2020. In that respect, population-based modeling has been applied to meaningful effect in biomechanics by capturing the effect of anatomical variation in many body regions (Rao et al, 2013;Bischoff et al, 2014;Campbell and Petrella, 2016).…”

“…In the area of face recognition, it has been documented that in order to representatively achieve population coverage, at least 250 samples are required (Claes, 2007). Similar numbers of training samples have recently been described to be required in osteological models (Audenaert, 2019). In order to apply statistical shape modeling in the evaluation of joint mechanics, one must consider the multiple structures comprising the joint, their interdependencies and their relative alignment (Rao et al, 2013).…”

“…The model was previously validated in terms of population coverage and model specificity, generalizability and accuracy. It is composed following principal component analysis of dens corresponding meshes obtained from 544 lower limb segmentations (362 male and 182 female training samples) (Styner et al, 2003;Audenaert, 2014, 2019, Almeida et al, 2016Audenaert et al, 2019c). After segmentation and registration of the anatomical shape entries, male and female shape entries were separated and used for the construction of two distinct PCA models to describe shape variance Mathematically, each model was described using the following equation:…”

Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge

“…Recent developments in medical imaging significantly increased the accuracy of the three-dimension computational anatomical representation, enhancing the anatomical differences within a determined population (Almeida et al, 2016;Audenaert et al, 2019). Hence, combining the use of magnetic resonance imaging (MRI) with musculoskeletal models will provide us an insight on lower limb kinematics on patient-specific anatomies.…”