IEEE J Biomed Health Inform. 2021 Aug;25(8):2958-2966. doi: 10.1109/JBHI.2021.3054494. Epub 2021 Aug 5.
Orthognathic surgical outcomes rely heavily on the quality of surgical planning. Automatic estimation of a reference facial bone shape significantly reduces experience-dependent variability and improves planning accuracy and efficiency. We propose an end-to-end deep learning framework to estimate patient-specific reference bony shape models for patients with orthognathic deformities. Specifically, we apply a point-cloud network to learn a vertex-wise deformation field from a patient's deformed bony shape, represented as a point cloud. The estimated deformation field is then used to correct the deformed bony shape to output a patient-specific reference bony surface model. To train our network effectively, we introduce a simulation strategy to synthesize deformed bones from any given normal bone, producing a relatively large and diverse dataset of shapes for training. Our method was evaluated using both synthetic and real patient data. Experimental results show that our framework estimates realistic reference bony shape models for patients with varying deformities. The performance of our method is consistently better than an existing method and several deep point-cloud networks. Our end-to-end estimation framework based on geometric deep learning shows great potential for improving clinical workflows.
正颌手术的结果在很大程度上依赖于手术规划的质量。自动估计参考面颅骨形状可显著降低经验依赖性变异性，提高规划的准确性和效率。我们提出了一种端到端的深度学习框架，用于估计正颌畸形患者的特定于患者的参考骨性形状模型。具体来说，我们应用点云网络从患者变形的骨性形状（表示为点云）中学习顶点变形场。然后，使用估计的变形场来校正变形的骨性形状，以输出特定于患者的参考骨性表面模型。为了有效地训练我们的网络，我们引入了一种从任何给定的正常骨骼合成变形骨骼的模拟策略，从而生成了一个相对较大且多样化的形状训练数据集。我们的方法使用合成和真实患者数据进行了评估。实验结果表明，我们的框架可以为具有不同畸形的患者估计出逼真的参考骨性形状模型。我们的方法的性能始终优于现有的方法和几种深度点云网络。我们基于几何深度学习的端到端估计框架在改善临床工作流程方面显示出巨大的潜力。
