3D Tooth Research Articles

Transformer based 3D tooth segmentation via point cloud region partition

Transformer based 3D tooth segmentation via point cloud region partition

Generating Tooth Organoids Using Defined Bioorthogonally Cross-Linked Hydrogels.

Generating teeth in vitro requires mimicking tooth developmental processes. Biomaterials are essential to support 3D tooth organoid formation, but their properties must be finely tuned to achieve the required biomimicry for tooth development. For the first time, we used bioorthogonally cross-linked hydrogels as defined 3D matrixes for tooth developmental engineering, and we highlighted how their properties play a pivotal role in enabling 3D tooth organoid formation in vitro. We prepared hydrogels by mixing gelatin precursors modified either with tetrazine (Tz) or norbornene (Nb) moieties. We tuned the hydrogel properties (E = 2-7 kPa; G' = 500-1500 Pa) by varying the gelatin concentration (8% vs 12% w/V) and stoichiometric ratio (Tz:Nb = 1 vs 0.5). We encapsulated dental epithelial-mesenchymal cell pellets in a library of hydrogels and identified a hydrogel formulation that enabled successful growth kinetics and morphogenesis of tooth germs, introducing a defined tunable platform for tooth organoid engineering and modeling.

Educational potential of 3D teeth models in virtual reality

Educational potential of 3D teeth models in virtual reality

Automatic 3D Teeth Reconstruction From Five Intra-Oral Photos Using Parametric Teeth Model.

Orthodontic treatment is a lengthy process that requires regular in-person dental monitoring, making remote dental monitoring a viable alternative when face-to-face consultation is not possible. In this study, we propose an improved 3D teeth reconstruction framework that automatically restores the shape, arrangement, and dental occlusion of upper and lower teeth from five intra-oral photographs to aid orthodontists in visualizing the condition of patients in virtual consultations. The framework comprises a parametric model that leverages statistical shape modeling to describe the shape and arrangement of teeth, a modified U-net that extracts teeth contours from intra-oral images, and an iterative process that alternates between finding point correspondences and optimizing a compound loss function to fit the parametric teeth model to predicted teeth contours. We perform a five-fold cross-validation on a dataset of 95 orthodontic cases and report an average Chamfer distance of 1.0121 mm2 and an average Dice similarity coefficient of 0.7672 on all the test samples in the cross-validation, demonstrating a significant improvement compared with the previous work. Our teeth reconstruction framework provides a feasible solution for visualizing 3D teeth models in remote orthodontic consultations.

Analysis of Three-Dimensional Tooth Movement: A Comparative Study Between Digital Dental Models and Craniofacial Models.

This study aims to validate the efficacy of using a digital dental model (DM) with reference to the palatal region of interest (PROI) for assessing orthodontic tooth movement (TM)by comparing it with the analysis of a computed tomography (CT) model with reference to the cranial region of interest (CROI). Thirty-four patients (mean age: 21 years and 11 months) with jaw deformities underwent DM and CT scans before and after presurgical orthognathic treatment. Linear and angular measurements during TM were conducted in three dimensions using both DM and CT to assess reliability. DM analysis with PROI registration exhibited high levels of reproducibility, with minimal standard errors in X, Y, and Z displacements (<0.15 mm) and 0.43 degrees in angular change. CT analysis with CROI registration demonstrates similarly high reproducibility, with standard errors inferior to DM analysis (<0.20 mm). Bland-Altman analysis indicated agreement in linear changes of each X, Y, and Z displacement between DM and CT measurements, with limits of agreement (LOA) below 0.91 mm. The results of this study suggest that the PROI, focusing on the third palatal rugae and the horizontal part of the palatal vault, serves as a reliable reference region for evaluating three-dimensional (3D) tooth movement. Digital dental models offer distinct advantages including the absence of X-ray exposure, no metal artifacts, and the ability to generate high-resolution 3D models. The methodology demonstrated high precision and reproducibility, supporting its potential clinical utility in orthodontic treatment planning and assessment.

3D teeth micromovements with DIC technique and novel extraoral scanner

3D teeth micromovements with DIC technique and novel extraoral scanner

Cover Image

The cover image is based on the Article MRCM‐UCTransNet: Automatic and Accurate 3D Tooth Segmentation Network From Cone‐Beam CT Images by Xinyang Wen et al., https://doi.org/10.1002/ima.23139 image

DBGANet: Dual-Branch Geometric Attention Network for Accurate 3D Tooth Segmentation

DBGANet: Dual-Branch Geometric Attention Network for Accurate 3D Tooth Segmentation

High-precision teeth reconstruction based on automatic multimodal fusion with CBCT and IOS

In digital orthodontic treatment, the high-precision reconstruction of complete teeth, encompassing both the crown and the actual root, plays a pivotal role. Current mainstream techniques, prioritizing the high resolution of intraoral scanned models (IOS), are confined to using IOS data for orthodontic treatments. However, the lack of root information in the IOS data may lead to complications such as dehiscence. In contrast, Cone Beam Computed Tomography (CBCT) data encompasses comprehensive dental information with roots. Nonetheless, the radiative character of CBCT scans renders patients unsuitable for repeated examinations in a short time. In addition, lower scanning precision of CBCT leads to suboptimal teeth segmentation outcomes, hindering the accurate representation of dental occlusal relationships. Therefore, in order to fully utilize the complementarity between dental multimodal data, we propose a method for high-precision 3D teeth model reconstruction based on IOS and CBCT, which mainly consists of global rigid registration and local nonrigid registration. Specifically, we extract the priori information of dental arch curves for coarse alignment to provide a good initial position for the Iterative Closest Point (ICP) algorithm, and design a conformal parameterization method for a single tooth to effectively obtain the point correspondence between IOS and CBCT crowns. The rough crown of the CBCT will gradually fit towards the IOS through iterative optimization of nonrigid registration. The experimental results show that our method robustly fuses the advantageous features of IOS and CBCT. The 3D teeth model reconstructed by our method contains the high-precision crown of IOS and the real root of CBCT, which can be effectively used in clinical orthodontic treatment.

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning.

Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations. A Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss. The reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries. This article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation

In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.

TSRNet: A Dual-stream Network for Refining 3D Tooth Segmentation.

The field of 3D tooth segmentation has made considerable advances thanks to deep learning, but challenges remain with coarse segmentation boundaries and prediction errors. In this paper, we introduce a novel learnable method to refine coarse results obtained from existing 3D tooth segmentation algorithms. The refinement framework features a dual-stream network called TSRNet (Tooth Segmentation Refinement Network) to rectify defective boundary and distance maps extracted from the coarse segmentation. The boundary map provides explicit boundary information, while the distance map provides gradient information in the form of the shortest geodesic distance between the vertex and the segmentation boundary. Following well-designed rules, the two refined maps are utilized to move the coarse tooth boundaries toward their correct positions through an iterative refinement process. The two-stage refinement method is validated on both 3D tooth and segmentation benchmark datasets. Extensive experiments demonstrate that our method significantly improves upon the coarse results from baseline methods and achieves state-of-the-art performance. (Code will be publicly available at https://github.com/bibi547/TSRNet.git.).

Tooth Motion Monitoring in Orthodontic Treatment by Mobile Device-based Multi-view Stereo.

Nowadays, orthodontics has become an important part of modern personal life to assist one in improving mastication and raising self-esteem. However, the quality of orthodontic treatment still heavily relies on the empirical evaluation of experienced doctors, which lacks quantitative assessment and requires patients to visit clinics frequently for in-person examination. To resolve the aforementioned problem, we propose a novel and practical mobile device-based framework for precisely measuring tooth movement in treatment, so as to simplify and strengthen the traditional tooth monitoring process. To this end, we formulate the tooth movement monitoring task as a multi-view multi-object pose estimation problem via different views that capture multiple texture-less and severely occluded objects (i.e. teeth). Specifically, we exploit a pre-scanned 3D tooth model and a sparse set of multi-view tooth images as inputs for our proposed tooth monitoring framework. After extracting tooth contours and localizing the initial camera pose of each view from the initial configuration, we propose a joint pose estimation scheme to precisely estimate the 3D pose of each individual tooth, so as to infer their relative offsets during treatment. Furthermore, we introduce the metric of Relative Pose Bias to evaluate the individual tooth pose accuracy in a small scale. We demonstrate that our approach is capable of reaching high accuracy and efficiency as practical orthodontic treatment monitoring requires.

DTR-Net: Dual-Space 3D Tooth Model Reconstruction From Panoramic X-Ray Images.

In digital dentistry, cone-beam computed tomography (CBCT) can provide complete 3D tooth models, yet suffers from a long concern of requiring excessive radiation dose and higher expense. Therefore, 3D tooth model reconstruction from 2D panoramic X-ray image is more cost-effective, and has attracted great interest in clinical applications. In this paper, we propose a novel dual-space framework, namely DTR-Net, to reconstruct 3D tooth model from 2D panoramic X-ray images in both image and geometric spaces. Specifically, in the image space, we apply a 2D-to-3D generative model to recover intensities of CBCT image, guided by a task-oriented tooth segmentation network in a collaborative training manner. Meanwhile, in the geometric space, we benefit from an implicit function network in the continuous space, learning using points to capture complicated tooth shapes with geometric properties. Experimental results demonstrate that our proposed DTR-Net achieves state-of-the-art performance both quantitatively and qualitatively in 3D tooth model reconstruction, indicating its potential application in dental practice.

A progressive framework for tooth and substructure segmentation from cone-beam CT images

Background:Accurate segmentation of individual tooth and their substructures including enamel, pulp, and dentin from cone-beam computed tomography (CBCT) images is essential for dental diagnosis and treatment planning in digital dentistry. Existing methods for tooth segmentation based on CBCT images have achieved substantial progress; however, techniques for further segmentation into substructures are yet to be developed. Purpose:We aim to propose a novel three-stage progressive deep-learning-based framework for automatically segmenting 3D tooth from CBCT images, focusing on finer substructures, i.e., enamel, pulp, and dentin. Methods:In this paper, we first detect each tooth using its centroid by a clustering scheme, which efficiently determines each tooth detection by applying learned displacement vectors from the foreground tooth region. Next, guided by the detected centroid, each tooth proposal, combined with the corresponding tooth map, is processed through our tooth segmentation network. We also present an attention-based hybrid feature fusion mechanism, which provides intricate details of the tooth boundary while maintaining the global tooth shape, thereby enhancing the segmentation process. Additionally, we utilize the skeleton of the tooth as a guide for subsequent substructure segmentation. Results:Our algorithm is extensively evaluated on a collected dataset of 314 patients, and the extensive comparison and ablation studies demonstrate superior segmentation results of our approach. Conclusions:Our proposed method can automatically segment tooth and finer substructures from CBCT images, underlining its potential applicability for clinical diagnosis and surgical treatment.

A fine-grained orthodontics segmentation model for 3D intraoral scan data

With the widespread application of digital orthodontics in the diagnosis and treatment of oral diseases, more and more researchers focus on the accurate segmentation of teeth from intraoral scan data. The accuracy of the segmentation results will directly affect the follow-up diagnosis of dentists. Although the current research on tooth segmentation has achieved promising results, the 3D intraoral scan datasets they use are almost all indirect scans of plaster models, and only contain limited samples of abnormal teeth, so it is difficult to apply them to clinical scenarios under orthodontic treatment. The current issue is the lack of a unified and standardized dataset for analyzing and validating the effectiveness of tooth segmentation. In this work, we focus on deformed teeth segmentation and provide a fine-grained tooth segmentation dataset (3D-IOSSeg). The dataset consists of 3D intraoral scan data from more than 200 patients, with each sample labeled with a fine-grained mesh unit. Meanwhile, 3D-IOSSeg meticulously classified every tooth in the upper and lower jaws. In addition, we propose a fast graph convolutional network for 3D tooth segmentation named Fast-TGCN. In the model, the relationship between adjacent mesh cells is directly established by the naive adjacency matrix to better extract the local geometric features of the tooth. Extensive experiments show that Fast-TGCN can quickly and accurately segment teeth from the mouth with complex structures and outperforms other methods in various evaluation metrics. Moreover, we present the results of multiple classical tooth segmentation methods on this dataset, providing a comprehensive analysis of the field. All code and data will be available at https://github.com/MIVRC/Fast-TGCN.

Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery.

This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools. Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared. The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth. The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.

OrthoAligner: Image-based Teeth Alignment Prediction via Latent Style Manipulation.

In this paper, we present OrthoAligner, a novel method to predict the visual outcome of orthodontic treatment in a portrait image. Unlike the state-of-the-art method, which relies on a 3D teeth model obtained from dental scanning, our method generates realistic alignment effects in images without requiring additional 3D information as input and thus making our system readily available to average users. The key of our approach is to employ the 3D geometric information encoded in an unsupervised generative model, i.e., StyleGAN in this paper. Instead of directly conducting translation in the image space, we embed the teeth region extracted from a given portrait to the latent space of the StyleGAN generator and propose a novel {latent} editing method to discover a geometrically meaningful editing path that yields the alignment process in the image space. To blend the edited mouth region with the original portrait image, we further introduce a BlendingNet to remove boundary artifacts and correct color inconsistency. We also extend our method to short video clips by propagating the alignment effects across neighboring frames. We evaluate our method in various orthodontic cases, compare it to the state-of-the-art and competitive baselines, and validate the effectiveness of each component.

TeethGNN: Semantic 3D Teeth Segmentation with Graph Neural Networks.

In this paper, we present TeethGNN, a novel 3D tooth segmentation method based on graph neural networks (GNNs). Given a mesh-represented 3D dental model in non-Euclidean domain, our method outputs accurate and fine-grained separation of each individual tooth robust to scanning noise, foreign matters (e.g., bubbles, dental accessories, etc.), and even severe malocclusion. Unlike previous CNN-based methods that bypass handling non-Euclidean mesh data by reshaping hand-crafted geometric features into regular grids, we explore the non-uniform and irregular structure of mesh itself in its dual space and exploit graph neural networks for effective geometric feature learning. To address the crowded teeth issues and incomplete segmentation that commonly exist in previous methods, we design a two-branch network, one of which predicts a segmentation label for each facet while the other regresses each facet an offset away from its tooth centroid. Clustering are later conducted on offset-shifted locations, enabling both the separation of adjoining teeth and the adjustment of incompletely segmented teeth. Exploiting GNN for directly processing mesh data frees us from extracting hand-crafted feature, and largely speeds up the inference procedure. Extensive experiments have shown that our method achieves the new state-of-the-art results for teeth segmentation and outperforms previous methods both quantitatively and qualitatively.

Accuracy of CAD-CAM Surgically Guided Tooth Autotransplantation Using Guided Templates and Custom-designed Osteotomes in Human Cadaver Mandibles

IntroductionA major challenge in dentistry is the replacement of teeth lost prematurely due to trauma, caries, or malformations; especially in growing patients. The aim of this study was to assess the accuracy of CAD-CAM surgically guided tooth autotransplantation in cryopreserved cadaver mandibles using guided templates and custom-designed osteotomes. MethodsCryopreserved human cadaver heads were digitized and scanned using an intraoral optical scanner and a large-volume cone beam computed tomography device. First, virtual surgical planning was performed to create a 3D tooth replica, 2 surgical guides, and a custom-made osteotome for each single-rooted tooth autotransplantation procedure/case. Surgical sockets were created in the selected mandibles using guided tooling consisting of an initial guided osteotomy with implant burs and a final guided osteotomy using custom osteotomes. After tooth autotransplantation, second large-volume cone beam computed tomography images of the 5 cadaver mandibles were obtained. The discrepancy in mm within the 3D space (apical and mesiodistal deviations) between the final position of the autotransplanted teeth and their digitally planned 3D initial position was calculated and analyzed statistically (P < .05). ResultsAll donor teeth were placed without incident within their newly created sockets in the real mandibles. The mean difference between the digitally planned root apex position and the final tooth position was 2.46 ± 1.25 mm. The mesiodistal deviation of the autotransplanted teeth was 1.63 ± 0.96 mm. ConclusionsThe autotransplantation of single-rooted teeth with custom-designed and 3D-printed surgical tooling provided promising results. The technique was able to create surgically prepared sockets that could accommodate transplanted teeth in mandibles.