School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Department of Oral Craniomaxillofacial, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
Med Image Anal. 2025 Jan;99:103350. doi: 10.1016/j.media.2024.103350. Epub 2024 Sep 19.
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.
术后面部外观预测对于外科医生制定正颌手术计划和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，这阻碍了它们在临床实践中的应用。基于深度学习的方法已经显示出提高计算效率和达到可比精度的潜力。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结构方面存在限制。此外，它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，我们提出了一种新的基于双图卷积的术后面部外观预测模型，该模型通过在欧几里得空间和测地空间中从面部网格构建的两个图上进行学习来考虑表面几何形状，并在双空间中传递骨骼运动到面部运动。我们进一步采用了一种从粗到精的策略，该策略首先对面部网格进行较少顶点的粗预测，然后添加更多顶点以获得更稳健的精细预测。在真实临床数据上的实验表明，我们的方法在定性和定量方面都优于最先进的基于深度学习的方法。
