Abstract
Orthognathic surgery consultation is essential to help patients understand the changes to their facial appearance after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. To overcome these challenges, this study aims to develop a fully automated pipeline that generates accurate and efficient 3D previews of postsurgical facial appearances for patients with orthognathic treatment without requiring additional medical images. The study introduces novel aesthetic losses, such as mouth-convexity and asymmetry losses, to improve the accuracy of facial surgery prediction. Additionally, it proposes a specialized parametric model for 3D reconstruction of the patient, medical-related losses to guide latent code prediction network optimization, and a data augmentation scheme to address insufficient data. The study additionally employs FLAME, a parametric model, to enhance the quality of facial appearance previews by extracting facial latent codes and establishing dense correspondences between pre- and post-surgery geometries. Quantitative comparisons showed the algorithm's effectiveness, and qualitative results highlighted accurate facial contour and detail predictions. A user study confirmed that doctors and the public could not distinguish between machine learning predictions and actual postoperative results. This study aims to offer a practical, effective solution for orthognathic surgery consultations, benefiting doctors and patients.
Abstract (translated)
正颌手术咨询对于帮助患者理解手术后面部外观的变化至关重要。然而，由于治疗前后数据有限以及治疗方法的复杂性，目前的可视化方法往往效率低下且不够准确。为了克服这些挑战，本研究旨在开发一个全自动流程，为接受正颌治疗的患者生成精准高效的术后三维面部预览，而无需额外的医学影像。该研究引入了新颖的美学损失函数，如嘴巴凸度和不对称性损失，以提高对面部手术预测的准确性。此外，它还提出了用于病人三维重建的专业参数模型、与医疗相关的损失来指导潜在代码预测网络优化以及一个数据增强方案来解决数据不足的问题。本研究进一步采用了FLAME（一种参数化模型），通过提取面部潜在代码并建立术前术后几何结构之间的密集对应关系，以提高面部外观预览的质量。定量比较显示了算法的有效性，定性结果突出了准确的面部轮廓和细节预测。一项用户研究表明，医生和公众无法区分机器学习预测与实际术后结果。本研究旨在为正颌手术咨询提供一个实用有效的解决方案，惠及医生和患者。
URL
https://arxiv.org/abs/2412.11045
