Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model

Sun, Diya; Pei, Yuru; Li, Peixin; Song, Guangying; Guo, Yukun; Zha, Hongbin; Xu, Tianmin

doi:10.1007/978-3-030-59719-1_68

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…The network in Zanjani et al Zanjani, Moin, Verheij, et al 2019) considers geometric information such as face normals, but advanced information such as shapes or jaws is not included. Sun et al (2020) and Lian et al (2019) designed networks to learn from meshes or vertices, but their models' robustness and generalization ability are yet unsatisfied or not verified for clinical applications. In our study, we sample rich geometric information from meshes during preprocessing.…”

Section: Discussionmentioning

confidence: 99%

“…Though with agreeable interpretability, these methods cannot generalize to various teeth morphologies and usually require human involvement for postcorrection. Current advances employed deep learning techniques to develop more generic solutions (Zhao et al 2006;Zou et al 2015;Li and Wang 2016;Xu et al 2018;Lian et al 2019;Zanjani, Moin, Verheij, et al 2019;Lian et al 2020;Sun et al 2020). These methods showed better performance than geometry-based approaches.…”

Section: Introductionmentioning

confidence: 99%

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Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

et al. 2021

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Digital dentistry plays a pivotal role in dental health care. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This article presents an accurate, efficient, and fully automated deep learning model trained on a data set of 4,000 intraoral scanned data annotated by experienced human experts. On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

et al. 2021

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“…Although recent learning-based methods have achieved impressive performance on 3D tooth instance segmentation [28,29,39], they rely heavily on a large number of data with dense manual annotations, such as labeling all points of every individual tooth from a dental model. Since annotating such training data is particularly time-consuming, it is hard to collect a large enough dataset to cover complex dental models in real-world, thus largely limiting the generalization of those learning-based segmentation methods [30,36,38].…”

Section: Introductionmentioning

confidence: 99%

DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation

Qiu¹,

Ye²,

Chen³

et al. 2022

Preprint

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Automatic tooth instance segmentation on 3D dental models is a fundamental task for computer-aided orthodontic treatments. Existing learning-based methods rely heavily on expensive point-wise annotations. To alleviate this problem, we are the first to explore a low-cost annotation way for 3D tooth instance segmentation, i.e., labeling all tooth centroids and only a few teeth for each dental model. Regarding the challenge when only weak annotation is provided, we present a dental arch prior-assisted 3D tooth segmentation method, namely DArch. Our DArch consists of two stages, including tooth centroid detection and tooth instance segmentation. Accurately detecting the tooth centroids can help locate the individual tooth, thus benefiting the segmentation. Thus, our DArch proposes to leverage the dental arch prior to assist the detection. Specifically, we firstly propose a coarse-to-fine method to estimate the dental arch, in which the dental arch is initially generated by Bezier curve regression, and then a graph-based convolutional network (GCN) is trained to refine it. With the estimated dental arch, we then propose a novel Arch-aware Point Sampling (APS) method to assist the tooth centroid proposal generation. Meantime, a segmentor is independently trained using a patch-based training strategy, aiming to segment a tooth instance from a 3D patch centered at the tooth centroid. Experimental results on 4, 773 dental models have shown our DArch can accurately segment each tooth of a dental model, and its performance is superior to the state-of-the-art methods.

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“…However, the detection of the aforementioned tooth landmarks and axes that are crucial for defining the normal occlusion is largely left to experienced dentists and performed manually. Only a few studies (e.g., [9], [10]) explored the possibility of automatic detection of dental landmarks for teeth alignment. To further boost the automation level of tooth alignment, this paper aims to provide an automatic, data-driven approach to extract, from different types of teeth, both the landmarks and the axes that are used in Andrews' Six Keys, as exemplified in Fig.…”

Section: Introductionmentioning

confidence: 99%

Dense Representative Tooth Landmark/axis Detection Network on 3D Model

Wei¹,

Cui²,

Zhu³

et al. 2021

Preprint

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Artificial intelligence (AI) technology is increasingly used for digital orthodontics, but one of the challenges is to automatically and accurately detect tooth landmarks and axes. This is partly because of sophisticated geometric definitions of them, and partly due to large variations among individual tooth and across different types of tooth. As such, we propose a deep learning approach with a labeled dataset by professional dentists to the tooth landmark/axis detection on tooth model that are crucial for orthodontic treatments. Our method can extract not only tooth landmarks in the form of point (e.g. cusps), but also axes that measure the tooth angulation and inclination. The proposed network takes as input a 3D tooth model and predicts various types of the tooth landmarks and axes. Specifically, we encode the landmarks and axes as dense fields defined on the surface of the tooth model. This design choice and a set of added components make the proposed network more suitable for extracting sparse landmarks from a given 3D tooth model. Extensive evaluation of the proposed method was conducted on a set of dental models prepared by experienced dentists. Results show that our method can produce tooth landmarks with high accuracy. Our method was examined and justified via comparison with the state-of-the-art methods as well as the ablation studies.

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Automatic Tooth Segmentation and Dense Correspondence of 3D Dental Model

Cited by 21 publications

References 20 publications

Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning

DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation

Dense Representative Tooth Landmark/axis Detection Network on 3D Model

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