Statistical Shape Modeling for Pediatric Skull Patient Analysis

Statistical Shape Model (SSM) for infant cranial analysis is challenging due to open sutures, which complicate the process of establishing anatomical correspondences between models. This study proposes a Wrap4D-based methodology for accurate preparation of newborn’s cranial samples used in SSM. The approach integrates skin and skull segmentation with a guided wrapping technique that supports suture filling through anatomically-guided surface adaptation, while ensuring high fidelity in shape alignment. SSM algorithm uses the prepared dataset of 75 normal infant skulls from Meyer Children’s Hospital IRCCS for the creation of a template, which serves as the reference model for defining a standard newborn’s cranial shape. The methodology offers a rapid, reproducible and anatomically accurate solution for modelling suture closure. Preliminary results led to a Generalization of 1.245 mm, a Specificity of 2.659 mm and a Compactness of 98% with 20 modes of variation. Finally, quantitative comparison with traditional wrapping techniques reveals Wrap4D superior anatomical preservation in the region of suture.