Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX 77030, USA.
Med Image Anal. 2024 Apr;93:103094. doi: 10.1016/j.media.2024.103094. Epub 2024 Jan 26.
In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy.
在颌骨畸形患者的正颌外科规划中，准确模拟骨运动后面部外观的变化至关重要。与传统的基于有限元法（FEM）的生物力学方法相比，基于深度学习的方法具有高效和稳健的建模替代方案，但是，目前的方法没有考虑面部软组织和骨性结构之间的物理关系，因此在准确性方面不如 FEM。在这项工作中，我们提出了一个注意对应辅助运动变换网络（ACMT-Net），通过点到点的注意对应矩阵，将面部软组织的变化与骨运动相关联，从而预测面部的变化。为了确保有效的训练，我们还引入了对比损失，以便使用基于 k-最近邻（k-NN）的聚类对 ACMT-Net 进行自监督预训练。对颌骨畸形患者的实验结果表明，与最先进的基于 FEM 的方法相比，我们提出的方法可以显著提高计算效率，并且具有相当的面部变化预测准确性。
