Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores.
翻译:在3D印刷生物兼容材料方面的进展使得病人专用植入器越来越受欢迎。然而,这些植入器的设计仍是一个乏味的、主要是人工的过程。现有的植入器生成自动化方法主要以3D U-Net结构为基础,其基础是下取样或贴合数据,这可能导致详细或背景信息丢失。继最近Difmulation 概率模型的成功之后,我们提出了一个植入生成新颖的方法,其基础是3D点云扩散模型和氧化化网络的组合。由于我们的扩散模型中的随机取样过程,我们可以提出一个不同植入器每个缺陷的组合,医生可以从中选择最合适的组合。我们在SkullBreak和SkullFix数据集上评估我们的方法,产生高质量的植入器并实现竞争性评估分数。</s>