An intelligent system for craniomaxillofacial defecting reconstruction

Abstract

Craniomaxillofacial defects caused by congenital or acquired reasons seriously affect patients' physical and mental health. How to accurately and objectively repair the morphology of craniomaxillofacial tissues and organs through surgery is a difficult problem, and the preoperative virtual design is crucial. Traditional preoperative virtual design methods include mirror technology, statistical shape model, and deformable template. However, these methods are complex, time-consuming, and only applicable to some types of defects. Therefore, a general, intelligent, and personalized craniomaxillofacial defect virtual reconstruction system is desired. To solve this problem, a novel deep learning method, RecGAN, is proposed in this paper. RecGAN can learn the bone morphology of normal people, repair the defect intelligently based on the patient's remaining bone, and fully adapt to the special conditions of different patients. Currently, there are no open-source maxillofacial data sets available. Thus, a new maxillofacial computed tomography image data set with 500 simulated cases and 100 clinical cases is constructed to train and validate the method. The experimental results show that RecGAN can effectively restore the normal bone and tissue morphology of the patient's craniomaxillofacial defect area, solve the problem that there is no objective repair method for craniomaxillofacial defect, and achieve the best effect in the same type of research. The proposed intelligent craniomaxillofacial defect virtual reconstruction system based on RecGAN is expected to be applied in future clinical practice.