Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing

翻译：三维几何体的计算机表示对于工程和科学领域的系统与过程模拟至关重要。在医学领域，尤其是生物力学和骨科中，获取并使用三维几何体对许多工作流程至关重要。然而，尽管存在多种获取有机结构三维几何体的工具，但鲜有研究使其适用于预期的医学目的。此外，许多现有工具属于专有软件，限制了其应用。本研究提出两种基于广义回归神经网络的新型算法及四个处理流程，用于执行网格变形和过盈调整。这些算法已通过实现和测试案例与现有算法进行对比验证，显示出性能提升。通过将基于径向基函数网络的现有技术转换为基于广义回归神经网络的实现，所提出算法表现出显著改进。这些算法的MATLAB实现及源代码已公开于以下地址：https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing