MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement
This work addresses the problem of inefficient and manual dental crown design for dentists, offering a more automated and accurate solution. It represents a strong specific gain over existing methods.
This paper tackles the problem of automated dental crown design, which traditionally requires extensive manual adjustments despite existing CAD systems. The authors propose MADCrowner, a margin-aware mesh generation framework that deforms an initial template and refines the output, significantly outperforming existing approaches in geometric accuracy and clinical feasibility.
Dental crown restoration is one of the most common treatment modalities for tooth defect, where personalized dental crown design is critical. While computer-aided design (CAD) systems have notably enhanced the efficiency of dental crown design, extensive manual adjustments are still required in the clinic workflow. Recent studies have explored the application of learning-based methods for the automated generation of restorative dental crowns. Nevertheless, these approaches were challenged by inadequate spatial resolution, noisy outputs, and overextension of surface reconstruction. To address these limitations, we propose \totalframework, a margin-aware mesh generation framework comprising CrownDeformR and CrownSegger. Inspired by the clinic manual workflow of dental crown design, we designed CrownDeformR to deform an initial template to the target crown based on anatomical context, which is extracted by a multi-scale intraoral scan encoder. Additionally, we introduced \marginseg, a novel margin segmentation network, to extract the cervical margin of the target tooth. The performance of CrownDeformR improved with the cervical margin as an extra constraint. And it was also utilized as the boundary condition for the tailored postprocessing method, which removed the overextended area of the reconstructed surface. We constructed a large-scale intraoral scan dataset and performed extensive experiments. The proposed method significantly outperformed existing approaches in both geometric accuracy and clinical feasibility.