Abstract
Despite the growing importance of dental CBCT scans for diagnosis and treatment planning, generating anatomically realistic scans with fine-grained control remains a challenge in medical image synthesis. In this work, we propose a novel conditional diffusion framework for 3D dental volume generation, guided by tooth-level binary attributes that allow precise control over tooth presence and configuration. Our approach integrates wavelet-based denoising diffusion, FiLM conditioning, and masked loss functions to focus learning on relevant anatomical structures. We evaluate the model across diverse tasks, such as tooth addition, removal, and full dentition synthesis, using both paired and distributional similarity metrics. Results show strong fidelity and generalization with low FID scores, robust inpainting performance, and SSIM values above 0.91 even on unseen scans. By enabling realistic, localized modification of dentition without rescanning, this work opens opportunities for surgical planning, patient communication, and targeted data augmentation in dental AI workflows. The codes are available at: this https URL.
Abstract (translated)
尽管牙科CBCT（锥形束计算机断层扫描）在诊断和治疗计划中的重要性日益增加，但生成解剖结构真实且具有精细控制的扫描仍然在医学图像合成中面临挑战。在这项工作中，我们提出了一种新颖的基于条件扩散框架的方法，用于三维牙齿体积生成，并通过牙齿级别的二进制属性进行指导，这些属性允许对牙齿的存在和配置进行精确控制。我们的方法结合了基于小波去噪的扩散、FiLM（特征集成层模块）调节以及掩码损失函数，以专注于相关解剖结构的学习。 我们在包括添加或移除牙齿和全口齿列合成等多样化的任务中评估了模型的表现，并使用配对相似性和分布相似性度量进行评价。结果显示，在FID分数低的情况下表现出强大的真实性和泛化能力、稳健的修补性能以及在未见过的扫描上的SSIM值超过0.91。 通过允许在不重新扫描的情况下实现牙齿结构的真实和局部修改，这项工作为手术规划、患者沟通及牙科AI工作流程中的定向数据增强提供了新的机会。代码可在此网址获得：this https URL。
URL
https://arxiv.org/abs/2508.14276
