Tooth-Diffusion: Guided 3D CBCT Synthesis with Fine-Grained Tooth Conditioning
This work addresses the problem of realistic dental scan synthesis for surgical planning and data augmentation in dental AI, representing a domain-specific incremental advance.
The paper tackled the challenge of generating anatomically realistic 3D dental CBCT scans with fine-grained control over tooth presence and configuration, achieving strong fidelity with SSIM values above 0.91 and low FID scores across tasks like tooth addition and removal.
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.