Tooth segmentation on multimodal images using adapted segment anything model

With the increase in dental patient numbers and the ongoing digital transformation of dental hospitals, tooth segmentation has become increasingly crucial for the digital diagnosis, design, treatment, and customized appliance manufacturing of orthodontics, oral implant surgery, and prosthodontics. This study aims to adapt the Segment Anything Model (SAM) to the Tooth segmentation task for precise tooth segmentation performance. In this study, a novel tooth segmentation method-Tooth-ASAM-that harnesses the power of SAM was introduced. An adapter-based image encoder and mask decoder specifically tailored for adapting SAM to tooth images were designed. The proposed method was evaluated through rigorous evaluation of multimodal tooth images-including Cone Beam Computed Tomography (CBCT) images, panoramic X-rays, and natural teeth images captured by a micro-camera. The experimental results unequivocally show that Tooth-ASAM achieved remarkable performances across all four datasets, excelling in key metrics like the Dice coefficient, IoU, HD95, and ASSD. Furthermore, the proposed Tooth-ASAM delivered perceptually more accurate segmentation results than the state-of-the-art methods on the four tooth datasets. This research demonstrates that precise tooth segmentation performances were obtained by applying SAM and adaptation training strategy, making it highly suitable for clinical applications in orthodontics, oral implant surgery, and prosthodontics.