Abstract
Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, a deep convolutional generative adversarial network (DCGAN) called CTGAN is proposed to complete 3D mandibular cone beam computed tomography data from CT data. After extensive training, CTGAN was tested on 6 mandibular tumor cases, resulting in 3D virtual mandibular completion. We found that CTGAN can generate mandibles with different levels and rich morphology, including positional and angular changes and local patterns. The completion results are shown as tomographic images combining generated and natural areas. The 3D generated mandibles have the anatomical morphology of the real mandibles and transition smoothly to the portions without disease, showing that CTGAN constructs mandibles with the expected patient characteristics and is suitable for mandibular morphological completion. The presented modeling principles can be applied to other areas for 3D morphological completion from medical images.Clinical trial registration: This study is not a clinical trial. Patient data were only used for testing in a virtual environment. The use of the digital data used in this study was ethically approved.
Highlights
Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects
The goals of the training were to obtain a discriminator that could tell whether the drawing looked similar to a real mandible and to obtain a generator that could draw different tomographic images of the mandible
It is reasonable to believe that after further optimization, CTGAN will be practical. This model will greatly shorten the time and technical difficulty involved in mandibular completion and even achieve better results than traditional repair
Summary
Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. The completion results are shown as tomographic images combining generated and natural areas. The presented modeling principles can be applied to other areas for 3D morphological completion from medical images. Some diseases and procedures, such as mandibular trauma and radical surgery for oral cancer, often cause bone dysmorphia and d efects[1,2,3,4]. In such cases, the exact morphology of the normal mandible before disease is often difficult to obtain because the mandible is already deformed when the doctor first examines it. We hope to solve the expected reference model of the mandible, which conforms to both the morphology of the natural mandible and the healthy portion of the patient’s mandibular morphology
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
