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A framework for three-dimensional statistical shape modeling of the proximal femur in Legg-Calvé-Perthes disease
Luke G Johnson1,2, Joseph D Mozingo3, Penny R Atkins3,4,5
1School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
Purpose:
The pathomorphology of Legg-Calvé-Perthes disease (LCPD) is a key contributor to poor long-term outcomes such as hip pain, femoroacetabular impingement, and early-onset osteoarthritis. Plain radiographs, commonly used for research and in the clinic, cannot accurately represent the full extent of LCPD deformity. The purpose of this study was to develop and evaluate a methodological framework for three-dimensional (3D) statistical shape modeling (SSM) of the proximal femur in LCPD.
Methods:
We developed a framework consisting of three core steps: segmentation, surface mesh preparation, and particle-based correspondence. The framework aims to address challenges in modeling this rare condition, characterized by highly heterogeneous deformities across a wide age range and small sample sizes. We evaluated this framework by producing a SSM from clinical magnetic resonance images of 13 proximal femurs with LCPD deformity from 11 patients between the ages of six and 12 years.
Results:
After removing differences in scale and pose, the dominant shape modes described morphological features characteristic of LCPD, including a broad and flat femoral head, high-riding greater trochanter, and reduced neck-shaft angle. The first four shape modes were chosen for the evaluation of the model's performance, together describing 87.5% of the overall cohort variance. The SSM was generalizable to unfamiliar examples with an average point-to-point reconstruction error below 1mm. We observed strong Spearman rank correlations (up to 0.79) between some shape modes, 3D measurements of femoral head asphericity, and clinical radiographic metrics.
Conclusion:
In this study, we present a framework, based on SSM, for the objective description of LCPD deformity in three dimensions. Our methods can accurately describe overall shape variation using a small number of parameters, and are a step toward a widely accepted, objective 3D quantification of LCPD deformity.
