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：生物相容材料3D打印技术的进步使得患者特异性植入物日益普及，然而这些植入物的设计仍然是一个繁琐且主要依赖人工的过程。现有自动化植入物生成方法主要基于对降采样或分块数据处理的3D U-Net架构，这可能导致细节丢失或上下文信息缺失。受扩散概率模型最新成功的启发，我们提出了一种结合3D点云扩散模型与体素化网络的植入物生成新方法。由于扩散模型中的随机采样过程，我们可以为每个缺损部位生成一组不同的植入物候选方案，供临床医师从中选择最合适的方案。在SkullBreak和SkullFix数据集上的评估表明，我们的方法能够生成高质量植入物并取得具有竞争力的评分结果。