用牙齿属性控制3D牙科CT生成，实现精准修改。
Tooth-Diffusion: Guided 3D CBCT Synthesis with Fine-Grained Tooth Conditioning
- 基于小波去噪扩散模型，通过齿级二值属性引导生成
- 修复和新增牙齿时结构相似度超0.91，FID分数低
- 适合牙科手术规划与数据增强，无需重新扫描
尽管牙科CBCT扫描在诊断与治疗规划中日益重要，但在医学图像合成中仍难以生成具有精细控制的解剖学真实感扫描。本文提出一种新型条件扩散框架，用于3D牙科体积生成，通过齿级二值属性引导，实现对牙齿存在与否及配置的精确控制。方法结合小波去噪扩散、FiLM条件化与掩码损失函数，聚焦于相关解剖结构的学习。我们在多种任务上评估模型，包括牙齿添加、移除和全牙列合成，使用成对与分布相似性指标。结果表明，模型具备强保真度与泛化能力，FID得分低，修补性能稳健，在未见扫描上SSIM值仍高于0.91。该工作实现了无需重新扫描即可进行真实、局部的牙列修改，为外科规划、患者沟通与牙科AI中的目标数据增强开辟新可能。代码已开源：https://github.com/djafar1/tooth-diffusion。
原文摘要 · Abstract (English)
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: https://github.com/djafar1/tooth-diffusion.
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