# MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement Source: [MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement]() Collection index: [llms.txt]() Data updated: 2026-03-07T05:00:04.447Z Authors: Linda Wei, Chang Liu, Wenran Zhang, Yuxuan Hu, Ruiyang Li, Feng Qi, Changyao Tian, Ke Wang, Yuanyuan Wang, Shaoting Zhang, Dimitris Metaxas, Hongsheng Li Published: 2026-03-05T03:42:57.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Crown Generation on Dental Crown Design Dataset](): Crown Generation; Dental Crown Design Dataset; CD-L2 (Premolar): 0.176. - [Dental Crown Mesh Reconstruction on Dental Crown Dataset (test)](): Dental Crown Mesh Reconstruction; Dental Crown Dataset (test); Medial Area Difference (mm²): 4.37. - [Cervical margin segmentation on Dental Crown Dataset (test)](): Cervical margin segmentation; Dental Crown Dataset (test); Accuracy: 99.1. - [Cervical Margin Segmentation on Crown-generated preparations dataset](): Cervical margin segmentation; Crown-generated preparations dataset; Hausdorff Distance (mm): 0.545. ## Original sources and implementation [Original paper]() [PDF]()