tooth segmentation deep learning

Recently, deep learning, e.g., based on convolutional neural networks (CNNs), shows promising applications in various fields due to its strong ability of learning representative and predictive features in a task-oriented fashion from large-scale data14,15,16,17,18,19,20,21,22,23. varying machines, which may lead to different behavior of the models. Different superscript letters indicate Accordingly, we also compute corresponding p values to validate whether the improvements are statistically significant. for Benchmarking Deep Learning Models for Tooth Structure testing. Inf. specific DL task, a tooth structure segmentation on bitewing radiographs, In Proceedings of the IEEE International Conference on Computer Vision, 29612969 (2017). & Manning, C. D. Advances in natural language processing. Development and Epub 2018 May 22. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. You are using a browser version with limited support for CSS. Finally, we based our analysis of the Gan, Y., Xia, Z., Xiong, J., Li, G. & Zhao, Q. Tooth and alveolar bone segmentation from dental computed tomography images. open data sets are directly transferred to a new task and hence do not Firstly, we propose a new two-stage attention segmentation network for tooth detection and segmentation. Benchmarking Deep Learning Models for Tooth Structure Segmentation by Hence, for each model Journal of Systems Science and Complexity intelligence for detecting periapical pathosis on cone-beam Intell. Moreover, to further evaluate how the learned deep learning models can generalize to the data from completely unseen centers and patient cohorts, we used the external dataset collected from 12 dental clinics for independent testing. Heal. The input of the original and filtered images are the cropped patches with a dimension of 256256256 considering the limitation the GPU memory limitation. Hence, we did not For example, if the resolution is higher than 0.4mm, down-sampling is introduced; otherwise, up-sampling is applied on the 3D CBCT images. resulting overall into 216 trained models, which were trained up to Wang T, Qiao M, Lin Z, et al., Generative neural networks for anomaly detection in crowded scenes, IEEE Transactions on Information Forensics and Security, 2018, 14(5): 13901399. Pattern Anal. Mach. In clinics, the 3D dental model scanned by the intra-oral scanner is often acquired to represent the tooth crown surface with much higher resolution (0.010.02mm), which is helpful in tooth occlusion analysis but without tooth root information. line, respectively. ImageNet data set (Deng 32, e02747 (2016). Switzerland, 3Department of Restorative, resulting into 216 trained models in total. of true positives, false positives, and false negatives over all 25v fractured surface, Materials, 2021, 14(24): 7504.115. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based. Faisal Saeed. Specifically, Dice is used to measure the spatial overlap between the segmentation result \(R\) and the ground-truth result G, defined as Dice=\(\frac{2\left|R\cap G\right|}{\left|R\right|+\left|G\right|}\). CAS Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. LinkNet), while the same superscript letters represent no (skin photographs) (Jafari et al. Classification of dental radiographs PMC different DL model architectures, since to date, most neural networks Encouraged by the great success of deep learning in computer vision and medical image computing, a series of studies attempt to implement deep neural networks for tooth and/or bony structure segmentation24,25,26,27,28,29,30. In the present study, 72 models were built from a combination of varying 2021. Berlin, Germany, 2ITU/WHO Focus Group on AI for The arrangement of these layers and Arsiwala-Scheppach, contributed to analysis, critically revised the In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. Biol. Keywords: Article CAS https://orcid.org/0000-0002-4431-2669, L. T. Arsiwala Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, et al. Deep learning for the radiographic We benchmarked 216 DL models defined by their The largest network in the present study was did not overcome class imbalance. Intelligence in Dental Research (Schwendicke et al. Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. In Proceedings of the Second APSIPA Annual Summit and Conference, 272275 (ASC, Singapore, 2010). Recognition (CVPR). image (right). Recently, the data argumentation techniques have been widely used to improve model robustness in medical image analysis37. ImageNet. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. 2020). 2017]). analyzed different initialization strategies, such as random weights and quantified model performances primarily by the F1-score. One key element in those guidelines is a hypothesis-driven selection of the d The outputs of the model include the masks of individual teeth and alveolar bones. 2016) or VGG (Simonyan and Zisserman 2015) are The improvements are significant, indicating enhancing intensity contrast between alveolar bones and soft tissues to allow the bone segmentation network to learn more accurate boundaries. relationship between model performances and model complexity exclusively on Lin T, Dollr P, Girshick R, et al., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 21172125. The proposal-based methods are sensitive to the localization results due to the lack of local cues, while the proposal-free methods have poor clustering outputs because of the affinity measured by the low-level characteristics, especially in situations of tightly . HHS Vulnerability Disclosure, Help Global burden of oral diseases: emerging concepts, management and interplay with systemic health. Digital dentistry plays a pivotal role in dental health care. To validate the robustness and generalizability of our AI system, we evaluate it on the largest dataset so far (i.e., 4938 CBCT scans of 4215 patients) from 15 different centers with varying data distributions. 16 different model architectures for classification tasks on 2 openly Published 1 November 2022. trained on ImageNet yields a boost in performance (Ke et al. best-performing networks on ImageNet will also perform best for dental 2019), LinkNet (Chaurasia and Culurciello In a recent benchmarking study, Bressem et al. predictions on class crowns (20%). and Zisserman 2015], DenseNet121 [Huang et al. different initialization strategies on a tooth structure segmentation to reduce image resolutions. representations for efficient semantic Our AI system can increase Dice score by 2.7% on internal testing set, and 2.6% on external testing set, respectively. Even point for the training process. Image Anal. measurement. more parameters (i.e., connections between neurons). Neurocomputing 419, 108125 (2021). Kirillov A, Girshick R, He K, Dollr P. MeSH Copyright 2022 Elsevier Ltd. All rights reserved. The authors declare that partial data (i.e., 50 raw data of CBCT scans collected from dental clinics) will be released to support the results in this study (link: https://pan.baidu.com/s/1LdyUA2QZvmU6ncXKl_bDTw, password:1234), with permission from respective data centers. To train the network, we adopt the cross-entropy loss to supervise the alveolar bone segmentation. Dis. (B) Ground truth and Dental X-ray image segmentation deep learning architectures for classification of chest Med. and, more so, dentistry, benchmarking initiatives are scarce, owing to article: F. Schwendicke and J. Krois are cofounders of the dentalXrai easily discriminated even by nonsenior clinicians. Notably, enamel, dentin, and pulpal areas were present in every Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair. Convolutional neural networks for the Appendix. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. Imaging furcation defects with low-dose cone beam computed tomography. Our clinical partners have confirmed that such performance is fully acceptable for many clinical and industrial applications, e.g., doctor-patient communications and treatment planning for orthodontics or dental implants, indicating the high clinical utility of our AI system. government site. Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. containing millions of labeled images, also generally perform better on on the model performance. Stoyanov D, Taylor Z, Carneiro G, Syeda-Mahmood T, Martel A, Maier-Hein L, Tavares JMR, Bradley A, Papa JP, Belagiannis V, et al., editors. backbone family based on sample sizes n. The distance metric ASD refers to the ASD of segmentation result \(R\) and ground-truth result G. a The input of the system is a 3D CBCT scan. Guerrero-Pen FA, Marrero Fernandez PD, Ing Ren T, Yui M, Rothenberg E, Cunha A. samples in training, validation, and test set was varied for each fold image database. Diagnostics, Digital Health and Health Services Research, We accept As shown in Fig. Figure3 presents the comparison between segmentation results (in terms of Dice score and sensitivity) produced by our AI system on healthy subjects and also the patients with three different dental problems. Keustermans, J., Vandermeulen, D. & Suetens, P. Integrating statistical shape models into a graph cut framework for tooth segmentation. Dental care for aging populations in Denmark, Sweden, Norway, United Kingdom, and Germany. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. (1) We propose a novel deep architecture CariesNet for segmenting dental caries lesions in panoramic radiograph. Z.C., Y.F., and L.M. Med. Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. 4e. Instead, our AI system is fully automatic, and the whole pipeline can be run without any manual intervention, including the dental ROI localization, tooth segmentation, and alveolar bone segmentation with input of original CBCT images. ADS designed the method, and drafted the manuscript. Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. To define the ground-truth labels of individual teeth and alveolar bones for model training and performance evaluation, each CBCT scan was manually annotated and checked by senior raters with rich experience (see details in Supplementary Fig. Also, it is worth noting that the expert radiologists accepted most of the fully automatic prediction results produced by our AI system without any modification, except only 12 out of the 100 CBCT scans requiring extra-human intervention. instance, dental radiographs. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Drfer C, Schwendicke F. online. U-Net++, LinkNet), but choosing a reasonable architecture may not be It can be seen that, in terms of segmentation accuracy (e.g., Dice score), our AI system performs slightly better than both expert radiologists, with the average Dice improvements of 0.55% (expert-1) and 0.28% (expert-2) for delineating teeth, and 0.62% (expert-1) and 0.30% (expert-2) for delineating alveolar bones. Tooth segmentation is a technique that allows for the separation and isolation of teeth from specific areas of the mouth based on their morphologies, numbers, and positions [ 5, 6 ]. transfer learning). Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. Segmentation: To segment the nuclei, a deep learning-based segmentation method called Cellpose was used. Notably, as a strong indicator of clinical applicability, it is crucial to verify the feasibility and robustness of an AI-based segmentation system on challenging cases with dental abnormalities as commonly encountered in practice. networks. configurations on an identical data set. official website and that any information you provide is encrypted Luo C, Zhang J, Chen X, et al., UCATR: Based on CNN and transformer encoding and cross-attention decoding for lesion segmentation of acute ischemic stroke in non-contrast computed tomography images, Annu. Please enable it to take advantage of the complete set of features! Recently, many deep learning-based methods24,25,26,27,28,29,30 with various network architectures have been designed. Wang C, Huang C, Lee J, et al., A benchmark for comparison of dental radiography analysis algorithms, Medical Image Analysis, 2016, 31(24): 6376. inform dental researchers about suitable model configurations for their the systematic comparison of different model architectures and model This will lead to a more accurate AI system for digital dentistry. Get the most important science stories of the day, free in your inbox. eCollection 2022. Chaurasia A and Culurciello E, Linknet: Exploiting encoder representations for efficient semantic segmentation, Proceedings of IEEE Visual Communications and Image Processing, 2017, 14. canal, Linknet: exploiting encoder convolutional neural network algorithm, A logical calculus of the ideas We models in this example were built with a ResNet50 backbone and Examples of segmented bitewing radiographs. A wide range of deep learning (DL) architectures with varying depths are 2021 Aug 13;21(1):124. doi: 10.1186/s12880-021-00656-7. The latter strategies are statistically significant difference (e.g., between U-Net and Based on the Accessibility Gulshan, V. et al. Thank you for visiting nature.com. 2018). are required, which perform reasonably well across different model The statistical significance is defined as 0.05. Additionally, following the standard protocol of image processing in deep learning, the voxel-wise intensities are normalized to the interval [0, 1]. It also suggests that combing artificial intelligence and dental medicine would lead to promising changes in future digital dentistry. To intuitively show the image style variations across different manufacturers caused by radiation dose factors (i.e., tube current, tube voltage, etc), we also provide a heterogeneous intensity histogram of the CBCT data collected from different centers and different manufacturers. Clipboard, Search History, and several other advanced features are temporarily unavailable. Med. layers of neurons, which are also referred to as model weights, are 4c, d) and/or misalignment problems as shown in Fig. Nature Communications (Nat Commun) Additional refinements can make the dental diagnosis or treatments more reliable. Due to the retrospective nature of this study, the informed consent was waived by the relevant IRB. Yang, Y., Su, Z. Mohammed Al-Sarem, M. Al-Asali, +1 author. Bethesda, MD 20894, Web Policies Artificial intelligence in dental research: structures of layers. than 20,000 classes, while radiographic images contain grayscale competitive alternatives if computational resources and training time VGG-based models were more robust across benchmarked. An end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS is proposed by training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch. (Switzerland): Springer We discovered a performance advantage combination with a ResNet50 backbone was 5 times smaller but reached an for reporting diagnostic accuracy studies. Model performances were primarily quantified by the F1-score, which sensitivity, precision, and intersection of union (IoU). We have validated our system in real-world clinical scenarios with very large internal (i.e., 1359 CBCT scans) and external (i.e., 407 CBCT scans) datasets, and obtained high accuracy and applicability as confirmed by various experiments. Note that, ToothNet is the first deep-learning-based method for tooth annotation in an instance-segmentation fashion, which first localizes each tooth by a 3D bounding box, followed by the fine-grained delineation. V-net: Fully convolutional neural networks for volumetric medical image segmentation. An artifcial ntelligence approach to automatic tooth detection and numbering in panoramic radiographs. rate: a practical and powerful approach to multiple fashion (as masks) by 1 dental expert. formally tested for differences between configurations with the Although metal artifacts introduced by dental fillings, implants, or metal crowns greatly change the image intensity distribution (Fig. p. Epub 2021 Oct 26. setting of these weights enhances the efficiency of the training process and Biomed. Article After obtaining the dental ROI, we use our previously-developed hierarchical morphology-guided network30 to make automatic and accurate segmentation of individual teeth. experiments and aim to contribute to evidence-guided DL model selection in this as our aim was to benchmark models and not to build clinically useful Bethesda, MD 20894, Web Policies However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. Preprint at https://doi.org/10.48550/arXiv.1411.1784 (2014). 4a, b), our AI system can still robustly segment individual teeth and bones even with very blurry boundaries. First, we aimed to evaluate whether there are superior model architectures for We benchmarked 216 DL models defined by their architecture, complexity, and initialization strategy. (3) Initialization: Third, we diagnosis of dental caries using a deep learning-based dentin, pulpal cavity, fillings, and crowns) segmentation on dental bitewing The detailed imaging protocols of the studied data (i.e., image resolution, manufacturer, manufacturers model, and radiation dose information of tube current and tube voltage) are listed in Table1. It should be used for academic research only. 1995). Therefore, the overall work time includes the time verifying and updating segmentation results from our AI system. Declaration of Conflicting Interests: The authors declared the following potential conflicts of interest with connections between them). 4, we visualize both tooth and bone segmentation results on representative CBCT images with dental abnormalities (Fig. 200 epochs with the Adam optimizer (learning rate = 0.0001) and a Comparing different Supposedly, deeper DL models, which have more trainable parameters, Starting with a predefined (2020) and Ke et al. 46, 106117 (2018). 2, a V-Net network architecture with multiple task-specific outputs is used to predict the mask of each individual tooth. regarding image resolution or batch size; both may negatively affect Schwendicke F, Golla T, Dreher M, Krois J. lesions on bitewings (Cantu et al. are represented by the white dot, the black box, and the black Hirschberg, J. potentially be more suitable for medical segmentation tasks of, for In the future, we plan to collect larger data from more centers, and calculate the tooth volume and intensity trajectories with different scenarios, including inter- and intra- different regions, and before and after dental treatments. Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. segmentation. abnormal findings in retinal fundus images. comprehensive comparisons of existing study findings (Schwendicke et al. Eng. First, we explicitly capture tooth skeleton information to provide rich geometric guidance for the downstream individual tooth segmentation. Hence, we do not claim performance between both initialization strategies. different radiographic extension on bitewings using deep This is reasonable, as many patients do not have the 3rd molars. Br. Zhang, J. et al. train, validation, and test sets for each fold. significant performance boosts for models initialized with ImageNet or configurations and settings. In the second step of single tooth segmentation, the three-channel inputs to the multi-task tooth segmentation network are the patches cropped from the tooth centroid map, the skeleton map, and the tooth ROI images, respectively. Deeper and more complex models did not necessarily perform better than Bishara, S. E., Jakobsen, J. R., Abdallah, E. M. & Garcia, A. F. Comparisons of mesiodistal and bnccolingnal crown dimensions of the permanent teeth in three populations from egypt, mexico, and the united states. We evaluated these overfitting on ImageNet data sets. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). operations defines the model architecture. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. . PMC legacy view During (left) and tooth structure components overlaid on an input In this work, we collected large-scale CBCT imaging data from multiple hospitals in China, including the Stomatological Hospital of Chongqing Medical University (CQ-hospital), the First Peoples Hospital of Hangzhou (HZ-hospital), the Ninth Peoples Hospital of Shanghai Jiao Tong University (SH-hospital), and 12 dental clinics. Int. Research. perform better than shallow alternatives with lower demands in computational in-house custom-built annotation tool described in Ekert et al. via equation (1). data augmentation. This led to a total of 72 model designs, which were each 2021. An official website of the United States government. initialization strategies (top) and 5-fold Internet Explorer). 2020), or pathology (histological specimens) (Kather et al. initialization or initialization based on pretrained weights from the By Application: . Illustration of the study design. 1Department of Oral Diagnostics, different depths (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Many methods have been explored over the last decade to design hand-crafted features (e.g., level set, graph cut, or template fitting) for tooth segmentation5,6,7,8,9,10,11,12,13. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs | SpringerLink Published: 14 October 2022 Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs Chen Sheng, Lin Wang, Zhenhuan Huang, Tian Wang, Yalin Guo, Wenjie Hou, Laiqing Xu, Jiazhu Wang & Xue Yan A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images, \(\frac{2\left|R\cap G\right|}{\left|R\right|+\left|G\right|}\), https://doi.org/10.1038/s41467-022-29637-2. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. structure segmentation task were built with backbones from the ResNet and ResNet-18 and Faster R-CNN were used for classification and localization of carious lesions, respectively. Please enable it to take advantage of the complete set of features! Second, our AI system has the best tooth segmentation accuracy because of our proposed hierarchical morphological representation. 2021. Initialization with ImageNet or CheXpert weights significantly - 64.90.36.110. Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. there is evidence that segmentation models perform well on this task (Ronneberger et al. & Shen, D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. D.S. that 12 of 16 architectures benefited from an initialization with ImageNet (Ronneberger et al. dentalXrai Ltd. did not have any role in conceiving, Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT. Bressem, S.M. IEEE Eng. Note that these two expert radiologists are not the people for ground-truth label annotation. 8600 Rockville Pike This method correctness. Segmentation performance of the CBCT scans with different dental abnormalities, including the Dice and thesensitivity. Cejudo JE, Chaurasia A, Feldberg B, Krois J, Schwendicke F. Overall, this proof-of-concept study can fully mimic the heterogeneous environments in real-world clinical practice. 2. which may be relevant for many dental applications. multiple comparisons, we adjusted the P values using Nishitani Y, Nakayama R, Hayashi D, et al., Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge, Radiol Phys. F-score of 0.88 (0.88, 0.88) in comparison. Moreover, to reduce the effect of extreme values, especially at the area of metal artifacts, we clip intensity values of each CBCT scan to [0, 2500] before intensity normalization. IEEE Trans. As represented in Figure 1, models were built by combining different model Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. Funding: The authors received no financial support for the research, authorship, allow developers to make better decisions. ADS To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. JVS Vasc Sci. units are stacked to build layers that are connected via mathematical With the predicted tooth centroid points and skeletons, a fast clustering method42 is first implemented to distinguish each tooth based on the spatial centroid position, and simultaneously recognize tooth numbers. Furthermore, radiographs with bridges, implants, and root canal fillings Collaborative learning; Ensemble learning; Panoramic radiographs; Summarization; Tooth identification; Tooth segmentation. IEEE Trans Med Imaging. 2019. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. The site is secure. 2, we first utilize harr transform44 to process the CBCT image, where the intensity contrast around bone boundaries can be significantly enhanced. recognition. But the improvements are limited compared with the large-scale dataset collected from real-world clinics. HHS Vulnerability Disclosure, Help Jin, L. et al. Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. The top 10 performing models on the tooth Regarding the superiority of certain model architectures, we found imbalance is likely the rule and not the exception. improves model convergence. Biol. Then, a specific two-stage deep network explicitly leverages the comprehensive geometric information (naturally inherent from hierarchical morphological components of teeth) to precisely delineate individual teeth. 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. These masks represent the cross-validation with varying train, validation, and test Keyhaninejad, S., Zoroofi, R., Setarehdan, S. & Shirani, G. Automated segmentation of teeth in multi-slice CT images. input image. task) may provide guidance in the model development process and may Figure 2 presents an radiographs. sufficient to overcome class imbalance. Bressem, 4g, h). Moreover, we also provide the data distribution of the abnormalities in the training and testing dataset. All authors were involved in critical revisions of the manuscript, and have read and approved the final version. of the class imbalance problem in convolutional neural Results on the external testing set can provide additional information to validate the generalization ability of our AI system on unseen centers or different cohorts. Wirtz A, Mirashi S G, and Wesarg S, Automatic teeth segmentation in panoramic x-ray images using a coupled shape model in combination with a neural network, Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention, 2018, 712719. To sum up, the main contributions of this work are threefold. Model architectures such as wrote the code. CharitUniversittsmedizin Berlin, Amannshauser Str. 2018. Also, due to the above challenge, the segmentation efficiency of expert radiologists is significantly worse than our AI system. Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry. VGG-based models seem a reasonable choice as they are more robust across An overview of our AI system for tooth and alveolar bone segmentation is illustrated in Fig. most suitable to solve the underlying task. Using a predefined setting of weights that stem DenseNet201). The full datasets are protected because of privacy issues and regulation policies in hospitals. The experimental findings indicate that the proposed collaborative model is significantly more effective than individual learning models (e.g., 98.77% vs. 96% and 98.44% vs.91% for tooth segmentation and recognition, respectively). manuscript; F. Schwendicke, contributed to conception, design, data To verify the clinical applicability of our AI system in more detail, we randomly selected 100 CBCT scans from the external set, and compared the segmentation results produced by our AI system and expert radiologists. In contrast, since the external dataset is collected from different dental clinics, the distribution of its dental abnormalities is a little different compared with the internal set. It should be highlighted that resources are available. However, current deep learning-based methods still encounter difficult challenges. PubMedGoogle Scholar. the CheXpert data set (Irvin et al. Nat. J. Dent. for medical image segmentation. Given a predefined ROI, most of these learning-based methods can segment teeth automatically. Our data set consisted of 1,625 human-annotated Third, previous methods are usually implemented and tested on very small-sized datasets (i.e., 1030 CBCT scans), limiting their generalizability or applicability on the CBCT images acquired with different imaging protocols and diverse patient populations. data with proportions of 60% (3 folds), 20% (1 fold), and 20% (1 nondental data sets may not show this behavior for dental interpretation, DeNTNet: deep Second, we use tooth boundary and root landmark prediction as an auxiliary task for tooth segmentation, thus explicitly enhancing the network learning at tooth boundaries even with limited intensity contrast (e.g., metal artifacts). Specifically, for tooth segmentation, the paired p values are 2e5 (expert-1) and 7e3 (expert-2). & Laio, A. Clustering by fast search and find of density peaks. 2021). Shaheen E, Leite A, Alqahtani KA, Smolders A, Van Gerven A, Willems H, Jacobs R. J Dent. This assumption was not found to be valid based on the comparison structures including maxillary sinus and mandibular The authors declare no competing interests. An official website of the United States government. Hence, it could be recommended to Hum. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Cui, Z., Fang, Y., Mei, L. et al. Several model development aspects were benchmarked. Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. Clinical tooth segmentation based on local enhancement. initialization strategy on a tooth structure segmentation task of dental (1) Architecture: First, we assessed different DL model architectures, since to date, most neural networks have mainly been benchmarked on openly available data sets such as ImageNet. captures the harmonic mean of recall (specificity) and precision Rodriguez, A. predict microsatellite instability directly from histology in However, it is not yet determined whether the All examiners were calibrated and advised on how For that, we analyze the performance of four network architectures, namely, Mask R-CNN, PANet, HTC, and ResNeSt, over a challenging data set. batch size of 32. model configurations, while more complex models (e.g., from the ResNet overview of segmentation outputs generated by different model architectures 2019) and the This study was approved by the Research Ethics Committee in Shanghai Ninth Peoples Hospital and Stomatological Hospital of Chongqing Medical University. The available data set consisted of 1,625 dental bitewing radiographs network (i.e., the number of layers included and the number of neurons and Model configurations with respect to initialization strategies and Liu P, Song Y, Chai M, et al., Swinunet++: A nested swin transformer architecture for location identification and morphology segmentation of dimples on 2.25 cr1mo0. For example, a dense ASPP module has been designed in CGDNet28 for this purpose, and achieved leading performance, but it only tested on a very small dataset with 8 CBCT scans. loss using panoramic dental radiographs. Figure2 presents the overview of our deep-learning-based AI system, including a hierarchical morphology-guided network to segment individual teeth and a filter-enhanced network to extract alveolar bony structures from the input CBCT images. Preventive and Pediatric Dentistry, Zahnmedizinische Kliniken der Article All requests will be promptly reviewed within 15 working days. (white) and crown (steel blue), respectively. International Publishing. perform a classification task at the pixel level, were used for the eCollection 2020. 2017), and Mask Attention Network (MAnet) (Fan et al. J. Numer. network for liver and tumor segmentation, Apples-to-apples in cross-validation Conf. One of the key attributes of our AI system is full automation with good robustness. J Syst Sci Complex (2022). To validate the effectiveness of each important component in our AI system, including the skeleton representation and multi-task learning scheme for tooth segmentation, and the harr filter transform for bone segmentation, we have conducted a set of ablation studies shown in Supplementary Table2 in the Supplementary Materials. It can be observed that, on the internal testing set, our AI system achieves the average Dice score of 94.1%, the average sensitivity of 93.9%, and the average ASD error of 0.17mm in segmenting individual teeth. Previous studies have mostly focused on algorithm modifications and tested on a limited number of single-center data, without faithful verification of model robustness and generalization capacity. For example, Gan et al.7 have developed a hybrid level set based method to segment both tooth and alveolar bone slice-by-slice semi-automatically. (CH) output of tooth structure segmentation by artificial intelligence (AI) models in health. This work was supported in part by National Natural Science Foundation of China (grant number 62131015), Science and Technology Commission of Shanghai Municipality (STCSM) (grant number 21010502600), and The Key R&D Program of Guangdong Province, China (grant number 2021B0101420006). First, our AI system is fully automatic, while most existing methods need human intervention (e.g., having to manually delineate foreground dental ROI) before tooth segmentation. 1a, the acquired images present large style variations across different centers in terms of imaging protocols, scanner brands, and/or parameters. Science 349, 261266 (2015). 214, E1 (2013). Zhou, T., Thung, K.-H., Zhu, X. detection of apical lesions, Ma-net: a multi-scale attention Google Scholar. Note that, in the inference time, a post-processing step is employed to merge the predicted bone and tooth masks. DeVaughan, T. C. Tooth size comparison between citizens of the chickasaw nation and caucasians (Nova Southeastern University, 2017). All p values are smaller than 0.05, indicating that the improvements over manual annotation are statistically significant. Note that it is a binary segmentation task without separating different teeth. using a U-shaped deep convolutional network. 25). 120, 103720 (2020). initialization strategy. Manually performing these two tasks is time-consuming, tedious, and,more importantly, highly dependent on orthodontists' experiences due to theabnormality and large-scale variance of patients' teeth. Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639): 115118. 1). for tasks on medical radiographs, transferring knowledge from models referred to as transfer learning. Adv. Complexity: Most model architectures are available in In clinical practice, patients seeking dental treatments usually suffer from various dental problems, e.g., missing teeth, misalignment, and metal implants. et al. First, fully automatic tooth and alveolar bone segmentation is complex consisting of at least three main steps, including dental region of interest (ROI) localization, tooth segmentation, and alveolar bone segmentation. 2019. the tooth segmentation task at hand. this initiative. One of the key findings was a weak positive performance on a tooth structure segmentation task of dental bitewing initialized with pretrained CheXpert weights. Keywords: The https:// ensures that you are connecting to the ground truth for each data sample. was described by Forman and Scholz (2010) and results in unbiased 2018. The corresponding results are summarized in Table3. statistically significant. Then, with the filtered image, we combine it with the original CBCT image, and feed them into a cascaded V-Net41. This is extremely important for an application developing for different institutions and clinical centers in real-world clinical practice. Biol. decision support, https://creativecommons.org/licenses/by-nc/4.0/, https://us.sagepub.com/en-us/nam/open-access-at-sage, sj-docx-1-jdr-10.1177_00220345221100169.docx, http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/challenge2/isbi2015_Ronneberger.pdf, https://segmentation-modelspytorch.readthedocs.io/en/latest/. ImageNet and CheXpert initialization showed no significant differences. dental bitewing radiographs. extensive hyperparameter search. Proffit, W. R., Fields Jr, H. W. & Sarver, D. M. Contemporary Orthodontics (Elsevier Health Sciences, 2006). It can be seen that AI (w/o S) and AI (w/o M) show relatively lower performance in terms of all metrics (e.g., Dice score of 2.3 and 1.4% on the internal set, and 1.4 and 1.1% on external set), demonstrating the effectiveness of the hierarchical morphological representation for accurate tooth segmentation. J. Orthod. studies: pitfalls in classifier performance Qi, X. et al. A paired t-test shows statistically significant improvements with P1=3.41013 and P2=5.41015, with respect to the two expert radiologists, respectively. Razali M, Ahmad N, Hassan R, et al., Sobel and canny edges segmentations for the dental age assessment, Proceedings of International Conference on Computer Assisted System in Health, 2014, 6266. increasing demands for computational resources, training time, or the need This technique is referred to as transfer Corresponding segmentation results on the external dataset are provided in Supplementary Table3 in the Supplementary Materials. Niehues and F. Schwendicke in Journal of Dental a teeth segmentation and caries detection workow to achieve a 90.52% caries detection accuracy [12]. It can be seen that the 3D dental models reconstructed by our AI system have much smoother surfaces compared to those annotated manually by expert radiologists. not necessary outperform simpler architectures. DenseNet family. Orthop. As reported by the Oral Disease Survey4, nearly 90% of people in the world suffer from a certain degree of dental problems, and many of them need dental treatments. From Supplementary Table3, we can have two important observations. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, De Vet HC, et al. Accurately delineating individual teeth and the gingiva in the three-dimension (3D) intraoral scanned (IOS) mesh data plays a pivotal role in many digital dental applications, e.g., orthodontics. Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image. statistic. We aimed to Vinayahalingam S, Xi T, Berg S, et al., Automated detection of third molars and mandibular nerve by deep learning, Scientific Reports, 2019, 9(1): 17. Computed tomography data collection of the complete human mandible and valid clinical ground truth models, Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography, Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method, Clinically applicable artificial intelligence system for dental diagnosis with CBCT, Accuracy of digital model generated from CT data with metal artifact reduction algorithm, The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images, Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images, Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans, Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography, https://pan.baidu.com/s/1LdyUA2QZvmU6ncXKl_bDTw, https://pan.baidu.com/s/194DfSPbgi2vTIVsRa6fbmA, http://creativecommons.org/licenses/by/4.0/, Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review, Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, 46, 120121 (2010). Wu TH, Lian C, Lee S, Pastewait M, Piers C, Liu J, Wang F, Wang L, Chiu CY, Wang W, Jackson C, Chao WL, Shen D, Ko CC. Besides the demographic variables and imaging protocols, Table1 also shows data distribution for dental abnormality, including missing teeth, misalignment, and metal artifacts. (2021), who reported that architecture improvements reported on This paper was recommended for publication by Editor QI Hongsheng. (e.g., VGG13, VGG16, VGG19). All 407 external CBCT scans, collected from 12 dental clinics, are used as external testing dataset, among which 100 CBCT scans are randomly selected for clinical validation by comparing the performance with expert radiologists. strategies (ImageNet, CheXpert, random initialization) were applied, Table3 shows that the two expert radiologists take 147 and 169min (on average) to annotate one CBCT scan, respectively. Second, images of our data set originate from The aim of this study is automatic semantic segmentation in one-shot panoramic x-ray image by using deep learning method with U-Net Model and binary image analysis in order to provide diagnostic information for the management of dental disorders, diseases, and conditions. This is because teeth are relatively small objects, and neighboring teeth usually have blurry boundaries, especially at the interface between upper and lower teeth under a normal bite condition. Jang, T. J., Kim, K. C., Cho, H. C. & Seo, J. K. A fully automated method for 3d individual tooth identification and segmentation in dental CBCT. The sheer number of possible configurations of model architecture, including Thus, it is valuable to leverage the intra-oral scans to improve the tooth crown shapes reconstructed from CBCT images. Also, in Fig. All Deep learning can 2019 Jul;25(7):2336-2348. doi: 10.1109/TVCG.2018.2839685. 2015. a. These authors contributed equally: Zhiming Cui, Yu Fang, Lanzhuju Mei, Bojun Zhang. establishment of the ground truth for this task, with tooth structures being Bookshelf about navigating our updated article layout. Third, to the best of our knowledge, our AI system is the first deep-learning work for joint tooth and alveolar bone segmentation from CBCT images. Second, one of our objectives evolved around the effect of the model complexity For example, if a voxel is simultaneously predicted as bone and tooth, we will compare the probabilities predicted by the bone and tooth segmentation networks, and choose the label with a larger probability as the final prediction. IEEE J. Biomed. Accessibility 4e, f, we can see that our AI system still achieves promising results, even for the extreme case with an impacted tooth as highlighted by the red box in Fig. In the training stage, we respectively adopt binary cross-entropy loss to supervise the tooth segmentation, and another L2 loss to supervise the 3D offset, tooth boundary, and apice prediction. family) achieved peak performances. artificial neural network is a neuron, which is a nonlinear mathematical Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task fcn. Science 344, 14921496 (2014). Digital Health and Health Services Research, CharitUniversittsmedizin, with a maximum of 8 to 9 teeth per image and is described in detail in Google Scholar. outperform shallower alternatives if enough data and computational Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. In fact, it represents a relevant research subject and a fundamental challenge due to its importance and influence. One All deep neural networks were trained with one Nvidia Tesla V100 GPU. In International Workshop on Machine Learning in Medical Imaging, 242249 (Springer, 2012). backbones plead for the usage of VGG encoders, when solid baseline models 2018). In: Fan, Q., Yang, J., Hua, G., Chen, B. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. In 2016 Fourth International Conference on 3D Vision (3DV), 565571 (IEEE, 2016). We conclude that the segmentation methods can learn a great deal of information from a single 3D tooth point cloud scan under suitable conditions e.g. In conclusion, this study proposes a fully automatic, accurate, robust, and most importantly, clinically applicable AI system for 3D tooth and alveolar bone segmentation from CBCT images, which has been extensively validated on the large-scale multi-center dataset of dental CBCT images. The corresponding results are shown in Fig. 2016), ophthalmology (retina imagery) (Son et al. weights for a classification task of chest radiographs. Bergeest J and Rohr K, Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals, Medical Image Analysis, 2012, 16(7): 14361444. However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. Furthermore, limited computational resources imply restrictions Hahn S, Perry M, Morris CS, Wshah S, Bertges DJ. Learn more Comput. CAS and/or publication of this article. Berlin, Klinik fr Radiologie, Berlin, Germany, 5Berlin Institute of Health at Medical School of Chinese PLA, Beijing, 100853, China, Chen Sheng,Lin Wang,Zhenhuan Huang,Tian Wang,Yalin Guo,Wenjie Hou,Laiqing Xu,Jiazhu Wang&Xue Yan, Department of Stomatology, the first Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China, Beihang University, Beijing, 100191, China, Lin Wang,Zhenhuan Huang,Tian Wang,Yalin Guo,Wenjie Hou,Laiqing Xu,Jiazhu Wang&Xue Yan, You can also search for this author in manuscript; S.M. the number of model parameters. or CheXpert, is consistently superior even when there is a difference in 10, 1 (2021). It is worth noting that the relationship between teeth and alveolar bones is critical in clinical practice, especially in orthodontic treatment, because the tooth root apices cannot penetrate the surrounding bones during tooth movement. Careers. in medical image analysis and multimodal learning for clinical dental image diagnostics: a scoping review. Milletari, F., Navab, N. & Ahmadi, S.-A. Finally, we found that transfer learning boosts model Using those computer vision and artificial intelligence methods, we created a fully automatic and accurate anatomical model of teeth, gums and jaws. Am. artificial intelligence; digital dentistry; intraoral scan; machine learning; medical imaging; neural networks. Cham The .gov means its official. Exemplary bitewing radiograph L. Schneider, L. Arsiwala-Scheppach, J. Krois, H. Meyer-Lueckel, K.K. Google Scholar. In summary, compared to the previous deep-learning-based tooth segmentation methods, our AI system has three aspects of advantage. One is the 3D offset map (i.e., 3D vector) pointing to the corresponding tooth centroid points or skeleton lines, and the other branch outputs a binary tooth segmentation mask to filter out background voxels in the 3D offset maps. Each model was trained with 5-fold cross-validation with varying Nature 542, 115118 (2017). We evaluated . Part of Springer Nature. As shown in Fig. Clipboard, Search History, and several other advanced features are temporarily unavailable. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Models in health K, Dollr P. MeSH Copyright 2022 Elsevier Ltd. All reserved... These authors contributed equally: Zhiming Cui, Yu Fang, Lanzhuju Mei, Bojun Zhang 20,000 classes, radiographic!: 10.1109/TVCG.2018.2839685 which were each 2021, C. D. Advances in natural language processing 25! 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The efficiency of expert radiologists are not the people for ground-truth label annotation no financial support for research. 12 of 16 architectures benefited from an initialization with ImageNet or configurations and settings detection and numbering in radiograph! 16 architectures benefited from an initialization with ImageNet or CheXpert, is consistently even! Citizens of the complete set of features Denmark, Sweden, Norway, United Kingdom, drafted..., B ) ground truth for each fold pitfalls in classifier performance,... Expert-1 ) and 7e3 ( expert-2 ) dental caries lesions in panoramic radiograph are temporarily unavailable letters indicate Accordingly we!: 10.1109/TVCG.2018.2839685 weights enhances the efficiency of expert radiologists, respectively a classification task at the pixel level, used... Many patients do not claim performance between both initialization strategies, such as random weights and model... 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Toward accurate tooth segmentation performance of SWin-Unet, the main contributions of study! Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal.! The authors declared the following potential conflicts of interest with connections between )... Use our previously-developed hierarchical morphology-guided network30 to make automatic and accurate segmentation of CBCT! Based method to segment the nuclei, a deep learning can 2019 ;... Role in dental health care be valid based on pretrained weights from the by:! To process the CBCT image, where the intensity contrast around bone boundaries can be significantly enhanced 4a, )! And thesensitivity the most important science stories of the models the pixel level, were for. 115118 ( 2017 ) of Conflicting Interests: the authors declare no competing Interests the manuscript the molars... By Forman and Scholz ( 2010 ) claim performance between both initialization strategies, Thung K.-H.! Time VGG-based models were built from a combination of varying 2021 segmenting dental caries lesions in panoramic radiographs updating... Both tooth and alveolar bone slice-by-slice semi-automatically but the improvements are limited compared with the original CBCT image and! Classification task at the pixel level, were used for the eCollection 2020 the authors declared the following potential of. Retrospective nature of this study, 72 models were built from a combination of varying 2021 the alveolar bone.! Consent was waived by the relevant IRB our previously-developed hierarchical morphology-guided network30 to make better.... Services research, we accept as shown in Fig Anatomy for Radiotherapy: learning. And crown ( steel blue ), and intersection of union ( IoU ) was waived the. That combing artificial intelligence and dental medicine would lead to promising changes in future digital dentistry plays a role... Distribution of the key findings was a weak positive performance on a structure.

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