Soft-tissue Driven Craniomaxillofacial Surgical Planning

• URL: http://arxiv.org/abs/2307.10954v1
• Date: Thu, 20 Jul 2023 15:26:01 GMT
• Title: Soft-tissue Driven Craniomaxillofacial Surgical Planning
• Authors: 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
• Abstract summary: In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. 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.
• Score: 13.663130604042278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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