Soft-tissue Driven Craniomaxillofacial Surgical Planning
Xi Fang Daeseung Kim Xuanang Xu Tianshu Kuang Nathan Lampen Jungwook Lee Hannah H. Deng Jaime Gateno Michael A. K. Liebschner James J. Xia Pingkun Yan
In CMF surgery, the planning of bony movement to achieve a desired facial
outcome is a challenging task. Current bone driven approaches focus on
normalizing the bone with the expectation that the facial appearance will be
corrected accordingly. However, due to the complex non-linear relationship
between bony structure and facial soft-tissue, such bone-driven methods are
insufficient to correct facial deformities. Despite efforts to simulate facial
changes resulting from bony movement, surgical planning still relies on
iterative revisions and educated guesses. To address these issues, we propose a
soft-tissue driven framework that can automatically create and verify surgical
plans. Our framework consists of a bony planner network that estimates the bony
movements required to achieve the desired facial outcome and a facial simulator
network that can simulate the possible facial changes resulting from the
estimated bony movement plans. By combining these two models, we can verify and
determine the final bony movement required for planning. The proposed framework
was evaluated using a clinical dataset, and our experimental results
demonstrate that the soft-tissue driven approach greatly improves the accuracy
and efficacy of surgical planning when compared to the conventional bone-driven
approach.