This study presents a fully automated pipeline for the morphometric analysis of the dens, based on an enhanced Statistical Shape Model of the C2 vertebra. Developed in MATLAB, the pipeline integrates semantic segmentation directly into the shape model, enabling precise and reproducible identification of anatomical regions without manual intervention. Unlike deep learning approaches, the proposed framework is well-suited to applications involving limited or heterogeneous data. The model was trained on 25 C2 vertebrae from the VerSe dataset and tested on three independent specimens. Evaluation of model compactness, generalization, and specificity confirmed the robustness of the Statistical Model. Morphometric parameters are extracted in reference to a local coordinate system derived from the mean C2 shape, ensuring anatomical consistency across specimens. The morphometric results for the dens fell within the anatomical ranges reported in the literature, supporting the method’s validity. While not intended for clinical use due to dataset limitations, this work lays the foundation for scalable, reproducible, and anatomically informed morphometric analysis of the dens.