A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision

The proposed automated annotation system achieves efficient and precise annotation of 3D facial soft tissue landmarks through standardized facial template construction, 3D facial nonlinear registration, and personalized landmark transfer. This system provided significant convenience for the measurement of three-dimensional landmarks of soft tissues, the analysis of facial soft tissue morphology, and the diagnosis, treatment, and efficacy evaluation of malocclusion. Additionally, The personalized template approach enhances annotation precision for severe facial deformities, broadening clinical applicability.