MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement

TL;DR

This paper introduces MADCrowner, a margin-aware framework for dental crown design that combines template deformation and margin segmentation to improve accuracy and clinical usability.

Contribution

The paper presents a novel margin-aware mesh generation framework with a deformable template and margin segmentation, addressing limitations of previous learning-based dental crown design methods.

Findings

Significantly improved geometric accuracy over existing methods

Enhanced clinical feasibility of automated crown design

Effective boundary constraint and postprocessing techniques

Abstract

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.