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

TL;DR

Inspired by the clinic manual workflow of dental crown design, CrownDeformR is designed to deform an initial template to the target crown based on anatomical context, which is extracted by a multi-scale intraoral scan encoder.

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.

Figures (20)

• Figure 1: Dental Crown Designing Procedure. With the patient's intraoral scan, the technician utilizes a CAD system to extract the cervical margin, select and modify a digital template of the target tooth, culminating in the design of the final crown.
• Figure 2: The visualization of surface reconstruction for a certain dental crown. (a) The original crown mesh. (b) point cloud with normal vectors of the crown mesh. (c) Reconstructed surface of (b) by Poisson surface reconstruction. The bottom of the reconstructed surface is compulsorily sealed due to the intrinsic constraints of the reconstruction algorithm.
• Figure 3: The workflow of MADCrowner. The input IOS and the cervical margin identified by CrownSegger are sent to CrownDeformR to generate a watertight crown by deforming and refining an initial template. The extraneous regions are excised, guided by the margin line, to obtain the result.
• Figure 4: Architecture of CrownSegger. The CrownSegger fuses both the point-wise and voxel-wise features and predicts the segmentation of the prepared abutment. The margin line is obtained by extracting the boundary of the segmentation mask.
• Figure 5: Overall architecture of CrownDeformR. CrownDeformR consists of three components: IOS feature extraction, Crown Template Deformation, and Coarse Crown Refinement. For each generated crown mesh, we utilize the margin line identified by CrownSegger to excise the extraneous regions and solve the intrinsic watertightness issue introduced by DPSR.