METHODS: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.
RESULTS: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.
CONCLUSIONS: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.
方法:利用表面网格理论和深度学习,我们的模型与传统的点云方法不同,采用曲面三角形网格。我们使用网格卷积受限玻尔兹曼机(MCRBM)模型提取潜在变量,以生成三维变形场,旨在增强几何信息的保存和可解释性。
结果:对我们模型的实验评估表明,预测精度为91.2%,这代表了对传统的基于机器学习的方法的显著改进。
结论:所提出的模型为口腔颌面外科术前计划提供了一种有前途的新工具。它显著提高了术后面部轮廓预测下颌骨缺损重建的准确性,与以前的方法相比,提供了实质性的进步。