用于自动植入物生成的点云扩散模型
Point Cloud Diffusion Models for Automatic Implant Generation
AI总结：
针对患者特异性植入物设计依赖人工、现有3D U-Net易丢失细节的问题，提出结合3D点云扩散模型与体素化网络的生成方法，可为每个缺损提供多个候选，并在SkullBreak和SkullFix上取得有竞争力的结果。
摘要：
生物相容性材料3D打印技术的进步使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。现有的植入物自动生成方法主要基于在降采样数据或分块数据上运行的3D U-Net架构，这可能导致细节或上下文信息的丢失。受Diffusion Probabilistic Models（扩散概率模型）近期成功的启发，我们提出了一种结合3D点云扩散模型与体素化网络的新型植入物生成方法。由于扩散模型中的随机采样过程，我们能够针对每个缺损提出一组不同的植入物，供医生从中选择最合适的一个。我们在SkullBreak和SkullFix数据集上评估了该方法，生成了高质量植入物，并取得了具有竞争力的评估分数。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
