Postoperative facial appearance prediction is vital for surgeons to make orthognathic surgical plans and communicate with patients. Conventional biomechanical prediction methods require heavy computations and time-consuming manual operations which hamper their clinical practice. Deep learning based methods have shown the potential to improve computational efficiency and achieve comparable accuracy. However, existing deep learning based methods only learn facial features from facial point clouds and process regional points independently, which has constrains in perceiving facial surface details and topology. In addition, they predict postoperative displacements for all facial points in one step, which is vulnerable to weakly supervised training and easy to produce distorted predictions. To alleviate these limitations, we propose a novel dual graph convolution based postoperative facial appearance prediction model which considers the surface geometry by learning on two graphs constructed from the facial mesh in the Euclidean and geodesic spaces, and transfers the bone movements to facial movements in dual spaces. We further adopt a coarse-to-fine strategy which performs coarse predictions for facial meshes with fewer vertices and then adds more to obtain more robust fine predictions. Experiments on real clinical data demonstrate that our method outperforms state-of-the-art deep learning based methods qualitatively and quantitatively.

中文翻译：

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用于术后面部外观预测的颌面骨运动感知双图卷积方法


术后面部外观预测对于外科医生制定正颌手术计划以及与患者沟通至关重要。传统的生物力学预测方法需要大量的计算和耗时的手动操作，这阻碍了其临床实践。基于深度学习的方法已显示出提高计算效率并实现可比精度的潜力。然而，现有的基于深度学习的方法仅从面部点云中学习面部特征并独立处理区域点，这在感知面部表面细节和拓扑方面受到限制。此外，他们一步预测所有面部点的术后位移，这容易受到弱监督训练的影响，并且容易产生扭曲的预测。为了缓解这些限制，我们提出了一种基于双图卷积的术后面部外观预测模型，该模型通过学习由欧几里德空间和测地线空间中的面部网格构建的两个图来考虑表面几何形状，并将骨骼运动转移到双中的面部运动空间。我们进一步采用从粗到细的策略，对具有较少顶点的面部网格进行粗略预测，然后添加更多以获得更稳健的精细预测。对真实临床数据的实验表明，我们的方法在定性和定量上都优于基于最先进的深度学习的方法。