Efficient tooth gingival margin line reconstruction via adversarial learning. (September 2022)

• Record Type: Journal Article
• Authors: Tian, Sukun; Wang, Miaohui; Ma, Haifeng; Huang, Pan; Dai, Ning; Sun, Yuchun; Meng, Jianjun
• Is Part Of: Biomedical signal processing and control. Volume 78(2022)
• Publication Date: 2022-09
• Subjects: Gingival contour reconstruction -- Generative adversarial networks -- Dental applications -- Depth image -- Deep learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103954
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A new visual distance-based orthogonal projection method is proposed to establish gingival margin database. A novel gingival contour reconstruction network equipped with a residual structure-based generator and two-scale discriminator is adopted to reconstruct gingival margin. An efficient GML framework is developed to reconstruct the personalized gingival line for missing teeth. Abstract: Gingival margin morphology plays an important role in the functional and aesthetic restoration of denture. However, this is a challenging task, mainly because there are great differences in the gingival margin morphology between different individuals and different dental positions. To address this problem, we present a deep adversarial network-driven gingival margin line reconstruction (GMLR) framework to automatically obtain the personalized gingival contour for a partially edentulous patient. Specifically, we first establish the training database by a visual distance-based orthogonal projection method to realize the bidirectional reversible mapping between three-dimensional gingival model and two-dimensional depth representation. Then, the GMLR network consists of a dual generator model and a two-scale discriminator model to avoid the loss of the gingival contour details. The proposed generator uses the global-to-local scheme to reconstruct clear gingival contour images in an end-to-end manner, while two-scale discriminator aims to guide the generator to produce a globally consistent gingival contour result with finer details. In addition, a comprehensive loss function is presented to combine gingival contour details, structure and perceptual features. Finally, we propose to reconstruct the personalized gingival line by the polygon-based node insertion and the feature line reconstruction method via the tangent constraint. Experimental results demonstrate that, under the same conditions, the proposed method outperforms recent advances on the real-world dental database. Importantly, the reconstructed missing GMLs are basically harmonious with the adjacent teeth and have enough anatomical morphology.