Unsupervised learning of reference bony shapes for orthognathic surgical planning with a surface deformation network

Abstract

Purpose: The purpose of this study was to reduce the experience dependence during the orthognathic surgical planning that involves virtually simulating the corrective procedure for jaw deformities. Methods: We introduce a geometric deep learning framework for generating reference facial bone shape models for objective guidance in surgical planning. First, we propose a surface deformation network to warp a patient's deformed bone to a set of normal bones for generating a dictionary of patient-specific normal bony shapes. Subsequently, sparse representation learning is employed to estimate a reference shape model based on the dictionary. Results: We evaluated our method on a clinical dataset containing 24 patients, and compared it with a state-of-the-art method that relies on landmark-based sparse representation. Our method yields significantly higher accuracy than the competing method for estimating normal jaws and maintains the midfaces of patients’ facial bones as well as the conventional way. Conclusions: Experimental results indicate that our method generates accurate shape models that meet clinical standards.