Intra-oral scan segmentation using deep learning

BMC Oral Health

Research

Tabea Flügge¹  Tong Xi²  Steven Kempers³  Shankeeth Vinayahalingam⁴  Bram van Ginneken⁵  Tzu-Ming Harry Hsu⁶  Julian Schoep⁷  David Anssari Moin⁷  Marcel Hanisch⁸ 

[1] Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203, Berlin, Germany;Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands;Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands;Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany;Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands;MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, 02139, Cambridge, MA, USA;Promaton Co. Ltd, 1076 GR, Amsterdam, The Netherlands;Promaton Co. Ltd, 1076 GR, Amsterdam, The Netherlands;Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany;

关键词: Deep learning;    Artificial intelligence;    Intra-oral scan;    Computer-assisted planning;    Digital imaging;   

DOI  :  10.1186/s12903-023-03362-8

 received in 2023-04-07, accepted in 2023-08-26,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectiveIntra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning.Material and methodsAs a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions.ResultsThe model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges.ConclusionThe proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans.Clinical significanceDeep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

13690_2023_1170_Article_IEq3.gif

13690_2023_1170_Article_IEq1.gif

Fig. 1

42004_2023_995_Article_IEq42.gif

Fig. 4

Fig. 3

Fig. 3

Fig. 1

12888_2023_5172_Article_IEq23.gif

13690_2023_1170_Article_IEq111.gif

12888_2023_5172_Article_IEq22.gif

Fig. 2

Fig. 1

13690_2023_1170_Article_IEq108.gif

Fig. 1

12888_2023_5168_Article_IEq2.gif

Fig. 1

12888_2023_5168_Article_IEq1.gif

Fig. 5

Fig. 2

Fig. 4

Fig. 6

13690_2023_1170_Article_IEq98.gif

Fig. 43

Fig. 6

Fig. 1

Fig. 2