Shape morphing technique can accurately predict pelvic bone landmarks

Abstract

Diffeomorphic shape registration allows for the seamless geometric alignment of shapes. In this study, we demonstrated the use of a registration algorithm to automatically seed anthropological landmarks on the CT images of the pelvis. We found a high correlation between manually and automatically seeded landmarks. The registration algorithm makes it possible to achieve a high degree of automation with the potential to reduce operator errors in the seeding of anthropological landmarks. The results of this study represent a promising step forward in effectively defining the anthropological measures of the human skeleton.

Mechanical metric for skeletal biomechanics derived from spectral analysis of stiffness matrix

“…In Fig. 3 a relation between anthropometric distances defined in 31 and modal stiffnesses are shown. Only moderate correlations were observed for both sexes with the maximal positive value 0.49 with (CI95% [0.26, 0.67] and ) for a pair 7- for male.…”

Bone mineral density modeling via random field: normality, stationarity, sex and age dependence

“…The similarity of bone shapes was measured with a modified intensitybased criterion called the demons-like metric. This metric provides best accuracy/speed balance among the other metrics tested (mean-squared difference, cross-correlation, mutual information) [23,24]. In order to minimize a registration error, a template bone shape, which is an estimation of a sample mean shape, was estimated according to [25,23].…”

Mechanical Metric for Skeletal Biomechanics Derived from Spectral Analysis of Stiffness matrix

“…The shape of the pelvic bones were parametrized by a series of landmarks called B 1 , B 2 , ..., B 19 and associated distances M 1 , M 2 , ..., M 10 , see author's previous study 31 . These landmarks and distances have been adopted from DSP2 tool (Diagnose Sexuelle Probabiliste 57 ).…”

“…To decrease the operator error in construction of large number of FE models, the automatized generation of landmarks and boundary conditions was employed with the help of deformable registration algorithm 31 , see Figure 6. The template bone shape was estimated based on 58 and consequently between the template bone and all bones in datasets the symmetric, diffeomorphism map was computed by Symmetric registration method (SyN) with demon metric (ANTs library 59 ).…”

“…The best accuracy 0.62 for side classification was reached by static stiffness metric S for the first eigenpair. In Figure 3 a relation between anthropometric distances defined in 31 and modal stiffnesses are shown. Only moderate correlations were observed for both sexes with the maximal positive value 0.49 with (CI95%[0.26, 0.67] and p = 0.0004) for a pair 7-M 9 for male.…”