Development of predictive statistical shape models for paediatric lower limb bones. (October 2022) - Shi, Beichen
Highlights: Statistical shape models (SSM) of bones have been developed for adults, though these have limited applicability to paediatric populations. SSM of paediatric lower limb bones were built and were capable of accurately reconstructing bone geometric models from anatomical landmarks. The SSM can be used to reconstruct subject-specific bone geometry from readily accessible anatomical landmarks, avoiding the need for medical imaging. The built paediatric SSM can reconstruct paediatric lower limb bones more accurately than adult-based SSM. Abstract: Background and objective: Accurate representation of bone shape is important for subject-specific musculoskeletal models as it may influence modelling of joint kinematics, kinetics, and muscle dynamics. Statistical shape modelling is a method to estimate bone shape from minimal information, such as anatomical landmarks, and to avoid the time and cost associated with reconstructing bone shapes from comprehensive medical imaging. Statistical shape models (SSM) of lower limb bones have been developed and validated for adult populations but are not applicable to paediatric populations. This study aimed to develop SSM for paediatric lower limb bones and evaluate their reconstruction accuracy using sparse anatomical landmarks. Methods: We created three-dimensional models of 56 femurs, 29 pelves, 56 tibias, 56 fibulas, and 56 patellae through segmentation of magnetic resonance images taken from 29 typically developing children (15 females; 13 ± 3.5 years). The SSM for femur, pelvis, tibia, fibula, patella, haunch (i.e., combined femur and pelvis), and shank (i.e., combined tibia and fibula) were generated from manual segmentation of comprehensive magnetic resonance images to describe the shape variance of the cohort. We implemented a leave-one-out cross-validation method wherein SSM were used to reconstruct novel bones (i.e., those not included in SSM generation) using full- (i.e., full segmentation) and sparse- (i.e., anatomical landmarks) input, and then compared these reconstructions against bones segmented from magnetic resonance imaging. Reconstruction performance was evaluated using root mean squared errors (RMSE, mm), Jaccard index (0-1), Dice similarity coefficient (DSC) (0-1), and Hausdorff distance (mm). All results reported in this abstract are mean ± standard deviation. Results: Femurs, pelves, tibias, fibulas, and patellae reconstructed via SSM using full-input had RMSE between 0.89 ± 0.10 mm (patella) and 1.98 ± 0.38 mm (pelvis), Jaccard indices between 0.77 ± 0.03 (pelvis) and 0.90 ± 0.02 (tibia), DSC between 0.87 ± 0.02 (pelvis) and 0.95 ± 0.01 (tibia), and Hausdorff distances between 2.45 ± 0.57 mm (patella) and 9.01 ± 2.36 mm (pelvis). Reconstruction using sparse-input had RMSE ranging from 1.33 ± 0.61 mm (patella) to 3.60 ± 1.05 mm (pelvis), Jaccard indices ranging from 0.59 ± 0.10 (pelvis) to 0.83 ± 0.03 (tibia), DSC ranging from 0.74 ± 0.08 (pelvis) to 0.90 ± 0.02 (tibia), and Hausdorff distances ranging from 3.21 ± 1.19 mm (patella) to 12.85 ± 3.24 mm (pelvis). Conclusions: The SSM of paediatric lower limb bones showed reconstruction accuracy consistent with previously developed SSM and outperformed adult-based SSM when used to reconstruct paediatric bones.