Inside from outside: head-to-skull-and-muscles translation with statistical shape relation and multiple region-of-interest learning.

From skin to musculoskeletal prediction is a current challenge in biomechanics. Previous studies attempted head-to-skull prediction using statistical shape relationships, but they did not leverage local shape geometries and reduced muscles to simple 1-D action lines. We introduced Inside from Outside, a novel approach that reconstructs the bones of the skull and the facial muscles from external head surfaces. The method coupled Region-of-Interest (ROI) learning with Statistical Shape Relationship (SSR) modeling to capture detailed anatomical correspondences. We reconstructed 329 head and skull bone meshes from head-and-neck Computed Tomography (CT) scans. Multivariate regression models were employed to learn the statistical relationships among ROI regions. A ten-fold cross-validation procedure was used to estimate the optimal number of shape parameters, the multivariate regression method, and the prediction strategies. As a result, the prediction errors were 1.9293 ± 0.1838 mm, 1.4464 ± 0.3044 mm, and 1.4933 ± 0.2470 mm for the predicted skull bones, skull shapes, and muscle attachment points, respectively. Our novel SSR- and ROI-based approach achieved more reliable accuracy in the head-to-skull prediction problem. The proposed approach will be used to perform patient-specific, head-based simulations of facial expressions to personalize rehabilitation strategies for patients with facial palsy.