CarDiff: Anatomically Aware Dental Caries Synthesis using Diffusion

원문 초록

Synthetic medical image generation can help address limited annotated data in dental imaging, particularly for under-represented caries classes. In this work, we present CarDiff, a semi-supervised framework for dental caries synthesis in panoramic radiographs. The method combines paired mask-image learning with self-supervised generation from augmented masks, while incorporating anatomy-aware conditioning between lesion regions and surrounding structures. Semantic consistency is enforced through segmentation-guided supervision and adversarial anatomical alignment. Experiments on the MLUA-DC1000 dataset show that CarDiff achieves improved synthesis quality compared with existing semantic synthesis baselines, obtaining the best Fr\'echet Inception Distance (FID) (90.76) and Learned Perceptual Image Patch Similarity (LPIPS) (0.48) scores among evaluated methods. We further assess the generated radiographs for downstream segmentation augmentation across seven architectures. Using a 5x augmented training set improves average Dice score by up to 6.5%, with larger gains observed for medium and combined lesion classes. These results suggest that anatomically consistent diffusion synthesis may be useful for dental radiograph augmentation under limited supervision. The code is available at https://github.com/s4nyam/CarDiff.