Body Mass Classification from Skeletal Elements Using Landmark-Free Morphological Atlas Estimation with Diffeomorphic Shape Mapping.

Purpose: We investigate whether femur morphology is affected by body mass (BM). To establish deformations associated with obesity, we propose an atlas estimation framework based on diffeomorphic shape mapping that relaxes the point correspondence requirement common to many conventional shape modeling approaches. Methods: The study sample consisted of femora from 18 normal weight (BMI between 20-25) and 18 obese (BMI > 30) individuals (Texas State University Donated Skeletal Collection). Bone surface models (2,500 vertices and approximately 5,000 faces) were generated from CT scans of the specimens (512x512 matrix, 0.625x0.625x0.5 mm voxels). The surface models were input to an optimization algorithm that yielded an atlas representation of shape variability consisting of a mean bone template and diffeomorphic deformations matching the template onto each specimen. The accuracy of normal weight vs. obese classification using principal atlas deformation modes was established in leave-one-out experiments with Support Vector Machine (SVM) classifier. Results: We achieved 75% classification accuracy in leave-one-out SVM experiments, indicating the possibility of functional skeletal adaptations to increased body mass. By visualizing the bone surface deformation given by the SVM classification direction, we found that morphological alterations associated with obesity might include relative thickening of the femoral neck and the trochanters, and retroversion of the femoral head. Conclusions: The landmark-free atlas estimation algorithm enabled detection of morphological femur variants that might be predictive of elevated body mass.