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.

翻译：在颅颌面手术中，规划骨骼移动以实现理想的面部效果是一项具有挑战性的任务。当前骨骼驱动方法侧重于将骨骼恢复正常形态，期望面部外观随之得到相应纠正。然而，由于骨骼结构与面部软组织之间存在复杂的非线性关系，此类骨骼驱动方法不足以矫正面部畸形。尽管已有研究模拟骨骼移动所致的面部变化，但手术规划仍依赖于反复修正和经验性推测。为解决这些问题，我们提出了一种软组织驱动框架，可自动创建并验证手术方案。该框架包含一个骨骼规划器网络，用于估计实现预期面部效果所需的骨骼移动量；以及一个面部模拟器网络，可模拟所估计骨骼移动方案可能引发的面部变化。通过结合这两个模型，我们能够验证并确定规划所需最终骨骼移动量。我们在临床数据集上对所提出的框架进行了评估，实验结果表明，与传统骨骼驱动方法相比，软组织驱动方法显著提高了手术规划的准确性与有效性。