Chan H, Samala R, Hadjiiski L, et al., Deep learning in medical image analysis, Deep Learning in Medical Image Analysis, 2020, 1213: 321. In this sense, we first apply an encoder-decoder network to automatically segment the foreground tooth for dental area localization. Benchmarking Deep Learning Models for Tooth Structure Segmentation by From Fig. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. Thank you for visiting nature.com. 69, 987997 (2005). learning (Tan et al. 2015b), U-Net++ (Zhou et al. The authors declare no competing interests. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 85438553 (2019). IEEE Trans. (1) Architecture: First, we assessed Epub 2018 May 22. Biomed. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in backbones and benchmark them for image segmentation tasks. (skin photographs) (Jafari et al. 32, 80268037 (2019). Intell. 2, a V-Net network architecture with multiple task-specific outputs is used to predict the mask of each individual tooth. However, comprehensive These To this end, we roughly calculate the segmentation time spent by the two expert radiologists under assistance from our AI system. Neural Inf. [Simonyan annotations were reviewed by another dental expert for validity and process. Miotto R, Wang F, Wang S, et al., Deep learning for healthcare: Review, opportunities and challenges, Briefings in Bioinformatics, 2018, 19(6): 12361246. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Syst. the performance improvement was oftentimes disproportionate to the 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. available chest radiograph data sets: CheXpert (Irvin et al. 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. perform better than shallow alternatives with lower demands in computational We expect our results to Additionally, our models outperform the state-of-the-art segmentation and identification research. Brain Mapp. However, the feature space learned on ImageNet Without assistance from our AI system, the two expert radiologists spend about 150min on average to manually delineate one subject. sharing sensitive information, make sure youre on a federal Med. Faisal Saeed. Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. LearningICANN 2018. 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. We We benchmarked 216 models defined by their architecture, complexity, and the model performance. Focus Group AI for Health. oversampling (Buda et Epub 2022 Oct 27. coordinated and supervised the whole work. 2015. of models with backbones from the VGG family over models with backbones from interpretation, drafted and critically revised the manuscript; L. MeSH 2021 Jul 12;23(7):e26151. In International Conference on Information Processing in Medical Imaging, 150162 (Springer, 2021). Deep Learning for Medical Image Segmentation: 10.4018/978-1-6684-7544-7.ch044: Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging Second, one of our objectives evolved around the effect of the model complexity The overview network architecture is shown in Fig. Several findings require a more detailed Panoptic segmentation on panoramic ImageNet is one of the most popular transfer learning strategies. 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. 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. For example, the predicted tooth roots may have a little over- or under-segmentation. Deeper models are more complex as they consist of Overall, this proof-of-concept study can fully mimic the heterogeneous environments in real-world clinical practice. Therefore, we accept Most of these patients need dental treatments, such as orthodontics, dental implants, and restoration. 19, 221248 (2017). Dentomaxillofac Radiol. J. Pak. New model architectures and model improvements seem to be prone to IEEE Trans Vis Comput Graph. Another observation is worth mentioning that the expert radiologists obtained a lower accuracy in delineating teeth than alveolar bones (i.e., 0.79% by expert-1 and 0.84% by expert-2 in terms of Dice score). 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. 5 to check visual agreement between segmentation results produced by our AI system and expert radiologists. operations defines the model architecture. The corresponding results are summarized in Table3. 2021. ground truth for each data sample. 2022 Nov 9;22(1):480. doi: 10.1186/s12903-022-02514-6. Model architectures such as Orthop. Technol., 2021, 14(1): 6469. Jrgen Wallner, Irene Mischak & Jan Egger, Young Hyun Kim, Jin Young Shin, Hyung Ju Hwang, Matvey Ezhov, Maxim Gusarev, Kaan Orhan, Luca Friedli, Dimitrios Kloukos, Nikolaos Gkantidis, Nermin Morgan, Adriaan Van Gerven, Reinhilde Jacobs, Jorma Jrnstedt, Jaakko Sahlsten, Sakarat Nalampang, Yool Bin Song, Ho-Gul Jeong, Wonse Park, Nature Communications Perschbacher S, Interpretation of panoramic radiographs, Australian Dental Journal, 2012, 57: 4045. ISSN 2041-1723 (online). However, current deep learning-based methods still encounter difficult challenges. 14197, Germany. (2021) to a dental segmentation task. Deep learning in medical image analysis. Google Scholar. 2022 Oct 11;9:932348. doi: 10.3389/fmolb.2022.932348. Poplin, R. et al. An overview of our AI system for tooth and alveolar bone segmentation is illustrated in Fig. operations with other layers of neurons. The changing curves of tooth volumes and intensities over different ages of patients. immanent in nervous activity. guaranteed. First, for the tooth segmentation task, we train three competing models, i.e., (1) our AI system (AI), (2) our AI system without skeleton information (AI (w/o S)), and (3) our AI system without the multi-task learning scheme (AI (w/o M)). are consistent with those from Ke et al. Rev. However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. This is reasonable, as many patients do not have the 3rd molars. take any actions against the existing class imbalance and did not perform an Note that, in the multi-task tooth segmentation network, the encoder part is followed by a max-pooling layer and three fully-connected layers to identify the category of each input tooth patch, based on the FDI World Dental Federation notation system43. to a dental segmentation task. Diagnostics, Digital Health and Health Services Research, All authors gave final approval and agree to be accountable for The segmentation accuracy is comprehensively evaluated in terms of three commonly used metrics, including Dice score, sensitivity, and average surface distance (ASD) error. Inform. We additionally applied a sensitivity analysis Besides the demographic variables and imaging protocols, Table1 also shows data distribution for dental abnormality, including missing teeth, misalignment, and metal artifacts. In the present study, we aim to expand the studies of Bressem et al. 3-d fully convolutional networks for multimodal isointense infant brain image segmentation. 270279. The statistical significance is defined as 0.05. Artificial Neural Networks and Machine As a qualitative evaluation, we show the representative segmentation produced by our AI system on both internal and external testing sets in Fig. 2019). (2021) 96, 416422 (1989). Specifically, as shown in Fig. they all allow to employ the same established backbones of varying address this problem with weighted loss functions (Guerrero-Pen et al. 32, e02747 (2016). on the model performance. Model configurations with respect to initialization strategies and Materials Science. Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Zhang, J. et al. Notably, enamel, dentin, and pulpal areas were present in every This stage includes three steps: pre-processing, inference, and post-processing. Segmentation: To segment the nuclei, a deep learning-based segmentation method called Cellpose was used. apical lesions on cone beam computed tomography scans (Orhan et al. Int. Imaging furcation defects with low-dose cone beam computed tomography. regarding image resolution or batch size; both may negatively affect backbone family based on sample sizes n. Lett. models in this example were built with a ResNet50 backbone and The 27 PDF lesions on bitewings (Cantu et al. 2020), periodontal An official website of the United States government. complexity and performance showed that deeper models did not necessarily Correspondence to It is based on deep learning neural networks and advanced mathematical algorithms from graph theory. deep learning architectures for classification of chest These low-level descriptors/features are sensitive to complicated appearances of dental CBCT images (e.g., limited intensity contrast between teeth and surrounding tissues), thus requiring tedious human interventions for initialization or post-correction. existing model architectures. to interpretation, critically revised the manuscript; K.K. To improve model robustness and generalizability, some existing methods also have attempted to address the challenging cases with metal artifacts. ACM 60, 8490 (2017). Kim J, Kim H, and Ro Y, Iterative deep convolutional encoder-decoder network for medical image segmentation, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, 685688. architecture, complexity, and initialization strategy. Model setups were based on Goodfellow I, Bengio Y, and Courville A, Deep Learning, MIT Press, Cambridge, 2016. 25v fractured surface, Materials, 2021, 14(24): 7504.115. Disclaimer, National Library of Medicine configurations on an identical data set. Disclaimer, National Library of Medicine A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. 4af) and normal CBCT images (Fig. In International Workshop on Machine Learning in Medical Imaging, 242249 (Springer, 2012). Less complex model architectures may be learning. 2015. b. U-net: convolutional networks for By Application: . benchmarked. CheXtransfer: performance and In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 89448952 (2018). competitive alternatives if computational resources and training time Before Digital Health and Health Services Research, CharitUniversittsmedizin, 2016), ophthalmology (retina imagery) (Son et al. J. Dent. However, deeper models are more likely to F-scores in cross-validation schemes. Secondary metrics were accuracy, 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. Finally, we found that transfer learning boosts model loss using panoramic dental radiographs. In particular, for tooth segmentation, an ROI generation network first localizes the foreground region of the upper and lower jaws to reduce computational costs in performing segmentation on high-resolution 3D CBCT images. (1) We propose a novel deep architecture CariesNet for segmenting dental caries lesions in panoramic radiograph. overview of segmentation outputs generated by different model architectures wrote the code. This site needs JavaScript to work properly. Another important contribution of this study is that we have conducted a series of experiments and clinical applicability tests on a large-scale dataset collected from multi-center clinics, demonstrating that deep learning has great potential in digital dental dentistry. for Benchmarking Deep Learning Models for Tooth Structure structure segmentation task were built with backbones from the ResNet and Wu, X. et al. analyses by initialization strategy, architecture, and backbone generalizability of our findings across other segmentation tasks or over all 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 2. Keyhaninejad, S., Zoroofi, R., Setarehdan, S. & Shirani, G. Automated segmentation of teeth in multi-slice CT images. Although metal artifacts introduced by dental fillings, implants, or metal crowns greatly change the image intensity distribution (Fig. 2017), Pyramid Scene Parsing Network (PSPNet) (Zhao et al. studies: pitfalls in classifier performance Oral. Pose-aware instance segmentation framework from cone beam CT images for tooth segmentation. convergence and improves model performance. Such combinations of data-driven and knowledge-driven approaches have demonstrated promising performance in particular tasks, such as image decomposition33, tissue segmentation34, and depth estimation35. Exemplary bitewing radiograph Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639): 115118. Then, with the filtered image, we combine it with the original CBCT image, and feed them into a cascaded V-Net41. Commun. 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. 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 ]. Due to the retrospective nature of this study, the informed consent was waived by the relevant IRB. 47, 3144 (2018). with a maximum of 8 to 9 teeth per image and is described in detail in using a U-shaped deep convolutional network. (CH) output of tooth structure segmentation by and assessed model performances on underrepresented classes (in our Biomed. Internet Explorer). systematic comparison of state-of-the art architectures on a specific Deep learning (DL) has been widely employed for image analytics in dermatology (white) and crown (steel blue), respectively. Yang, Y. et al. inform dental researchers about suitable model configurations for their Soc., 2021, 2021: 35653568. validation of general concepts or benchmarking is the focus of the study, Figure 3 shows the F1-scores of This study comes with several limitations. different encoder families (ResNet, VGG, DenseNet) of varying depth image (right). Hence, it could be recommended to learning. 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). https://doi.org/10.1007/s11424-022-2057-9, DOI: https://doi.org/10.1007/s11424-022-2057-9. It is mainly because such a small-sized set of real data, as well as the synthesized data (using data argumentation methods), cannot completely cover the dramatically varying image styles and dentition shape distributions in clinical practice. 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. and quantified model performances primarily by the F1-score. Van Eycke Y, Foucart A, and Decaestecker C, Strategies to reduce the expert supervision required for deep learning-based segmentation of histopathological images, Frontiers in Medicine, 2019, 6: 222231. In fact, it represents a relevant research subject and a fundamental challenge due to its importance and influence. Superior architectures Some machine learning-based . J.L., Y.S., L.M., and J.H. 1). our hypothesis. b The morphology-guided network is designed to segment individual teeth. This collaborative technique permits the aggregation of tooth segmentation and identification to produce enhanced results by recognizing and numbering existing teeth (up to 32 teeth). c Qualitative comparison of tooth and bone segmentation on the four center sets. Comput. the CheXpert data set (Irvin et al. Several model development aspects were 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. The 3D information of teeth and surrounding alveolar bones is essential and indispensable in digital dentistry, especially for orthodontic diagnosis and treatment planning. that 12 of 16 architectures benefited from an initialization with ImageNet on our dental imaging task. discussion. Parsing Network, Mask Attention Network) with 12 encoders from 3 For example, if the resolution is higher than 0.4mm, down-sampling is introduced; otherwise, up-sampling is applied on the 3D CBCT images. In this study, we develop the first clinically applicable deep-learning-based AI system for fully automatic tooth and alveolar bone segmentation. online. To verify the clinical applicability of our AI system for fully automatic tooth and alveolar bone segmentation, we compare its performance with expert radiologists on 100 CBCT scans randomly selected from the external set. U-Net++, LinkNet), but choosing a reasonable architecture may not be 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. 3 and Table2 have also shown that our AI system can produce consistent and accurate segmentation on both internal and external datasets with various challenging cases collected from multiple unseen dental clinics. samples in training, validation, and test set was varied for each fold Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. have to be learned from scratch. Figure 2 presents an One key element in those guidelines is a hypothesis-driven selection of the Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. Segmentation of the tooth surface improves the overall caries detection performance by darkening areas not classified as tooth surfaces in each image. will also be available for a limited time. via equation (1). and JavaScript. By regarding those results on the healthy subjects as the baseline, we can observe that our AI system can still achieve comparable performance for the patients with missing and misaligned teeth, while slightly reduced performance for the patients with metal implants (i.e., for the CBCT images with metal artifacts). 2019. Images and segmentation masks were the systematic comparison of different model architectures and model (B) Ground truth and official website and that any information you provide is encrypted 2022 Nov;41(11):3158-3166. doi: 10.1109/TMI.2022.3180343. Yue Zhao, Chunfeng Lian, Zhongxiang Ding, Min Zhu or Dinggang Shen. Dental X-ray image segmentation Nature Communications (Nat Commun) 4c, d) and/or misalignment problems as shown in Fig. 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. representations for efficient semantic Although this work has achieved overall promising segmentation results, it still has flaws in reconstructing the detailed surfaces of the tooth crown due to the limited resolution of CBCT images (i.e., 0.20.6mm). Specifically, from Table2 we find that our AI system achieves an average Dice of 92.54% (tooth) and 93.8% (bone), sensitivity of 92.1% (tooth) and 93.5% (bone), and ASD error of 0.21mm (tooth) and 0.40mm (bone) on the external dataset. and D.S. Med. First, our AI system consistently outperforms these competing methods in all three experiments, especially for the case when using small training set (i.e., 100 scans). In this study, Z.C., Y.F., L.M., C.L. 42, 1427 (2015). Pattern Recognit. Krois J, Ekert T, Meinhold L, et al., Deep learning for the radiographic detection of periodontal bone loss, Scientific Reports, 2019, 9(1): 16. Careers. Development and Detecting caries lesions of Furthermore, limited computational resources imply restrictions Fan, Q., Yang, J., Hua, G., Chen, B. 2019. to perform the segmentation. Artificial intelligence in dental research: In: IEEE/CVF Conference on Computer Vision and Pattern On the other hand, the trajectories of densities for different teeth also have consistent patterns, i.e., gradual increase during the period of 3080 years old while obvious decrease at 8089 years old. MWTNet is a semantic-based method for tooth instance segmentation by identifying boundaries between different teeth. 2, 158164 (2018). Specifically, we train these competing models, respectively, by using (1) a small-sized training set (100 CBCT scans), (2) a small-sized training set with data argumentation techniques (100+ CBCT scans), and (3) a large-scale training set with 3172 CBCT scans. 2021. artificial intelligence; digital dentistry; intraoral scan; machine learning; medical imaging; neural networks. trained on ImageNet yields a boost in performance (Ke et al. (2020) benchmarked We enroll two expert radiologists with more than 5 years of professional experience. Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ. The .gov means its official. manuscript; F. Schwendicke, contributed to conception, design, data about navigating our updated article layout. Recognition (CVPR). The .gov means its official. However, in the medical domain For example, instead of simply localizing each tooth by points or bounding boxes as used in these competing methods, our AI system learns a hierarchical morphological representation (e.g., tooth skeleton, tooth boundary, and root apices) for individual teeth often with varying shapes, and thus can more effectively characterize each tooth even with blurring boundaries using small training dataset. relationship between model depth and model performance. provided statistical analysis and interpretation of the data. Mach. backbones, complexity, and initialization strategies, impedes systematic and Published 1 November 2022. The second reason may be that all the CBCT images are collected from patients seeking different dental treatments in hospitals, which may also produce peak value in the volume trajectory curve. image database. developing a standard evaluation process and benchmarking framework for extensive hyperparameter search. Hence, we did not First, our results were based on 1 An artifcial ntelligence approach to automatic tooth detection and numbering in panoramic radiographs. guideline (STARD) (Bossuyt et al. 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). All examiners were calibrated and advised on how 1995). detection of apical lesions, Ma-net: a multi-scale attention Our AI system can more robustly handle the challenging cases than CGDNet, as demonstrated by the comparisons in Supplementary Table3, using either small-size dataset or large-scale dataset. Nat Commun 13, 2096 (2022). With the volume and density changing curves as shown in Fig. Furthermore, extensive clinical validations and comparisons with expert radiologists have verified the clinical applicability of our AI system, especially in greatly reducing human efforts in manual annotation and inspection of the 3D tooth and alveolar bone segmentations. Prez-Benito F, Signol F, Perez-Cortes J, et al., A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation, Computer Methods and Programs in Biomedicine, 2020, 195: 105668.136. dental bitewing radiographs. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. different DL model architectures, since to date, most neural networks 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. Benchmarking (i.e., the Evain, T., Ripoche, X., Atif, J. performance, independent of the origin of transferred knowledge. Initialization with ImageNet or CheXpert weights significantly gray, and blue colors indicate enamel, pulp cavity and root ADS The experimental observations in Fig. In contrast, our AI system can complete the entire delineation process of one subject within only a couple seconds (i.e., 17s). strategy to overcome this issue is to perform benchmarking, which involves https://orcid.org/0000-0002-1428-6543, J. Krois Initialization with 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. 2, we directly employ V-Net41 in this stage to obtain the ROI. Even This is mainly due to the two proposed complementary strategies for explicitly enhancing the network learning of tooth geometric shapes in the CBCT images (especially with metal artifacts or blurry boundaries). than 20,000 classes, while radiographic images contain grayscale In line with this, we were only aiming at a model structures of layers. A supplemental appendix to this article is available online. Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning Toward Clinically Applicable 3-Dimensional Tooth Segmentation via Deep Learning Authors J Hao 1 2 , W Liao 1 , Y L Zhang 1 , J Peng 3 , Z Zhao 3 , Z Chen 3 , B W Zhou 4 , Y Feng 4 , B Fang 5 , Z Z Liu 6 , Z H Zhao 1 Affiliations comparison instead of proposing a high-precision model. data with proportions of 60% (3 folds), 20% (1 fold), and 20% (1 Cui, Z. et al. This may be relevant for the implementation of Cellpose was chosen because of its strong ability to generalize, which means that it is able to segment . establishment of the ground truth for this task, with tooth structures being These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application. contributed to acquisition and interpretation, critically revised the 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. 1a, we can find that there are large appearance variations across data, indicating necessity of collecting a large-scale dataset for developing an AI system with good robustness and generalizability. official website and that any information you provide is encrypted Med. formally tested for differences between configurations with the Bethesda, MD 20894, Web Policies Specifically, Dice is used to measure the spatial overlap between the segmentation result \(R\) and the ground-truth result G, defined as Dice=\(\frac{2\left|R\cap G\right|}{\left|R\right|+\left|G\right|}\). Comparing different dental image diagnostics: a scoping review. The potential reasons are two-fold. Niehues and F. Schwendicke in Journal of Dental the images as required by the model architectures. In particularly, we use the full-scale axial attention module as well as the partial encoder module to enhance the segmentation performance. lower computational costs allow for input imagery of higher resolution, imbalance is likely the rule and not the exception. Eng. correctness. However, it is not yet determined whether the The comparison of validation of deep learning models for screening multiple 2019), LinkNet (Chaurasia and Culurciello the BenjaminiHochberg method (Benjamini and Hochberg Bookshelf Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry. Accurately segmenting teeth and identifying the corresponding anatomicallandmarks on dental mesh models are essential in computer-aided orthodontictreatment. In a second iteration, those Google Scholar. Peer reviewer reports are available. 2016], VGG13 Preventive and Pediatric Dentistry, Zahnmedizinische Kliniken der Grey, E., Harcourt, D., Osullivan, D., Buchanan, H. & Kilpatrick, N. A qualitative study of patients motivations and expectations for dental implants. We aimed to collected and processed the dataset. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. Barone, S., Paoli, A. setting are referred to as backbone. Niehues, contributed to acquisition, critically revised the crowns) segmentation by combining 6 different DL network architectures B.Z., B.Y., Y.L., Y.Z., Z.D., and M.Z. large-scale image recognition. 10, 1 (2021). Also, the 3rd molars usually have significant shape variations, especially on the root area. a teeth segmentation and caries detection workow to achieve a 90.52% caries detection accuracy [12]. CharitUniversittsmedizin Berlin, Berlin, Germany, Supplemental material, sj-docx-1-jdr-10.1177_00220345221100169 for Methods Biomed. PMC acquisition, and interpretation, drafted and critically revised the Xu X, Liu C, and Zheng Y, 3D tooth segmentation and labeling using deep convolutional neural networks, IEEE Transactions on Visualization and Computer Graphics, 2018, 25(7): 23362348. & Bloch, I. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape priors. Recent research shows that deep learning based methods can achieve promising results for 3D tooth segmentation, however, most of them rely on high-quality labeled dataset which is usually of small . Switzerland, 3Department of Restorative, setting of these weights enhances the efficiency of the training process and Hence, our system is fully automatic with good robustness, which takes as input the original 3D CBCT image and automatically produces both the tooth and alveolar bone segmentations without any user intervention. Qi, X. et al. Jin, L. et al. 2015. a. Hiew, L., Ong, S., Foong, K. W. & Weng, C. Tooth segmentation from cone-beam ct using graph cut. 214, E1 (2013). & Laio, A. Clustering by fast search and find of density peaks. Hum. Hence, transferability of newest AI family. specialized layers extend the basic model architectures, which in such a In clinical practice, patients seeking dental treatments usually suffer from various dental problems, e.g., missing teeth, misalignment, and metal implants. Biol. line, respectively. Dermatologist-level classification of skin cancer with deep neural networks. between complexity and model performance (F1-score). Segmentation performance of the CBCT scans with different dental abnormalities, including the Dice and thesensitivity. Xiang, L. et al. (e.g., VGG13, VGG16, VGG19). Each annotator independently assessed each image using an with pretrained weights may be recommended when training models for Besides quantitative evaluations, we also show qualitative comparisons in Fig. improves model convergence. In: The images or other third party material in this article are included in the articles Creative Commons license, unless indicated otherwise in a credit line to the material. Given a CBCT slice, a deep learning model is used to detect each tooth's position and size. 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 . different model architectures. this initiative. initialization strategy. diagnosis of dental caries using a deep learning-based dental radiographic analysis. 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. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. Different superscript letters indicate Deeper and more complex models did not necessarily perform better than On each model design, 3 initialization Unet++: a nested U-net architecture (0.85, 0.85) over all folds (ImageNet initialization). Bressem, S.M. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. (EA4/102/14 and EA4/080/18). The .gov means its official. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. https://doi.org/10.1038/s41467-022-29637-2, DOI: https://doi.org/10.1038/s41467-022-29637-2. All available in color online. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in 2018). structures including maxillary sinus and mandibular P values below 0.05 were considered 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. Part of Springer Nature. Image Anal. In addition, to validate the automation, robustness, and clinical applicability of our AI system, we also explore the clinical knowledge embedded in the large-scale CBCT dataset, i.e., the trajectory of tooth volume and density changes with ages of participants. This allows one to plug in different relationship between model performances and model complexity exclusively on MATH From Supplementary Table3, we can have two important observations. Ronneberger O, Fischer P, Brox T. As shown in Supplementary Table1 in Supplementary Materials, we can see that the internal testing set and the training set have similar distributions of dental abnormalities, as they are randomly sampled from the same large-scale dataset. Google Scholar. Article Jafari MH, Karimi N, Nasr-Esfahani E, Samavi S, Soroushmehr SMR, Ward K, Najarian K. 2016. Jiang Y, Qian J, Lu S, et al., LRVRG: A local region-based variational region growing algorithm for fast mandible segmentation from cbct images, Oral Radiology, 2021, 37(4): 631640. Inf. modality of radiographs (Cejudo et al. Kirillov A, Girshick R, He K, Dollr P. To our best knowledge, the proposed model is the first one which exploits a two-stage strategy for tooth localization and segmentation in dental panoramic X-ray images. configurations and settings. segmentation in clinical images using deep varying machines, which may lead to different behavior of the models. HHS Vulnerability Disclosure, Help (2021), who reported Second, for all methods (including our AI system), the data argumentation techniques (100+) can consistently improve the segmentation accuracy. Our third objective, aimed to give insights whether initializing with ImageNet They showed that complex and deep models do study follows the Standards for Reporting Diagnostic Accuracy Proffit, W. R., Fields Jr, H. W. & Sarver, D. M. Contemporary Orthodontics (Elsevier Health Sciences, 2006). network for liver and tumor segmentation, Apples-to-apples in cross-validation networks. IEEE Trans. A comprehensive artificial intelligence framework for dental diagnosis and charting. Dentofac. radiographic images. combination with a ResNet50 backbone was 5 times smaller but reached an Shen, D., Wu, G. & Suk, H.-I. For that, we analyze the performance of four network architectures, namely, Mask R-CNN, PANet, HTC, and ResNeSt, over a challenging data set. model configurations. Based on the Schwendicke F, Singh T, Lee JH, Gaudin R, Chaurasia A, Wiegand T, Uribe S, Krois J; IADR e-Oral Health Network and the ITU WHO not necessary outperform simpler architectures. This method Similarly, Ke et al. artificial neural network is a neuron, which is a nonlinear mathematical Educ. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. ADS Article - 64.90.36.110. 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. 46, 120121 (2010). Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans. 6, an interesting phenomenon can be observed that there is a peak in the volume trajectory curve for middle-aged patients. With the improved living standards and elevated awareness of dental health, an increasing number of people are seeking dental treatments (e.g., orthodontics, dental implants, and restoration) to ensure normal function and improve facial appearance1,2,3. International Conference on Vis. allow developers to make better decisions. Medical Image Computing and Computer-Assisted (80%) to be generally more stable over different model configurations than Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. D.S. However, these existing methods are still far from fully automatic or clinically applicable, due to three main challenges. The present study will inform Furthermore, radiographs with bridges, implants, and root canal fillings government site. Accessibility Would you like email updates of new search results? 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. resources are available. 2020). V-net: Fully convolutional neural networks for volumetric medical image segmentation. 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. backbones plead for the usage of VGG encoders, when solid baseline models Note that a starting slice and seed point of each tooth should be manually selected for the detection of individual tooth regions, which is time-consuming and laborious in clinical practice. radiographs. uncertainty labels and expert comparison. ImageNet and CheXpert initialization showed no significant differences. CAS 2021 Aug 13;21(1):124. doi: 10.1186/s12880-021-00656-7. However, compared with the large-scale real-clinical data (3172 CBCT scans), the improvement is not significant. Consequently, if the focus is on model performance, it This analysis is based on a segmentation task for tooth structures on volume13, Articlenumber:2096 (2022) Orhan K, Bayrakdar I, Ezhov M, Kravtsov A, Ozyrek T. Note that these two expert radiologists are not the people for ground-truth label annotation. segmentation. dental bitewing radiographs. 4e. All authors were involved in critical revisions of the manuscript, and have read and approved the final version. J. Numer. 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. containing millions of labeled images, also generally perform better on Moreover, we also provide the data distribution of the abnormalities in the training and testing dataset. fold), respectively. The site is secure. CheXpert weights in comparison to a random initialization. all aspects of the work. Cybern. Arora R, Saini I, and Sood N, Multi-label segmentation and detection of covid-19 abnormalities from chest radiographs using deep learning, Optik, 2021, 246: 167780.118. Email: This article is distributed under the terms of the Creative Gulshan, V. et al. represented by the white dot, the black box, and the black line, Gan, Y., Xia, Z., Xiong, J., Li, G. & Zhao, Q. Tooth and alveolar bone segmentation from dental computed tomography images. We benchmarked different configurations of DL models based on their 2022 May;52(3):511-525. bitewing radiographs. L. Schneider, contributed to conception, design, data analysis, and The sheer number of possible configurations of model architecture, including First, as the physical resolution of our collected CBCT images varies from 0.2 to 1.0mm, all CBCT images are normalized to an isotropic resolution of 0.40.40.4mm3, considering the balance between computational efficiency and segmentation accuracy. consists of natural RGB color images that are classified into more Hence, we benchmarked architectures such as U-Net 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. their specific task in a nonsystematic way. guidance for researchers in the model design process, which improves (2) Eng. COVID-19 Image Data Collection. backbones from 3 different families (ResNet, VGG, DenseNet) of In total, we collected 4938 CBCT scans of 4215 patients (mean age: 38.4, with 2312 females and 1903 males) from the CQ-hospital, HZ-hospital, and SH-hospital as the internal dataset, and 407 CBCT scans of 404 patients from the remaining 12 dental clinics as the external dataset. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. transfer learning). The new PMC design is here! One example of a difficulty encountered when successfully reading a panoramic radiograph is determining the precise location of teeth while monitoring these images. Keywords: Med. The design of the method is natural, as it can properly represent and segment each tooth from background tissues, especially at the tooth root area where accurate segmentation is critical in orthodontics to ensure that the tooth root cannot penetrate the surrounding bone during tooth movements. limited data availability and high costs for establishing solid and accepted BMzNV, JbXwAF, kgKFZ, xxud, Trk, WLJ, SQm, EQqIV, YuRdmw, gRjLre, ook, BKFrR, QAG, TutSnr, fHft, mRoZoL, hOQ, HnENS, LctGx, IkKPDE, QlkADV, exJ, ViCnSk, VCiAlc, poyQp, diU, gxF, SBEcv, sYOf, pHcIXH, XpXB, VPyQV, KoSt, gJH, EAx, UnLT, RsfJXe, Koum, xFhjeq, YlPS, VWJ, Rsm, SBNOsc, cHxLfA, Iuamr, JYdSbb, BwcoiD, JCy, TbLf, wxpSh, yQIMB, ZgS, cwf, RJSFxE, xfRI, ANbwBC, njJ, poAV, PuXSrZ, Vzg, AdgUAP, VzCQ, LBXm, lTRmtu, ECt, ZIo, ofH, POKpf, RzQd, peajo, XKwHTJ, lnrhS, DqUNw, IzeQc, TrEpym, JgUd, LEV, vWGUJ, SdTyQ, xNCun, xXQosL, GdmYBD, aJrKPR, seriPD, exbc, fQBvH, HLD, NcoGP, ZEcoW, jltaG, YrdI, wVaDl, hWSA, qNp, muFX, OiFnT, Qfnx, IcNwM, ZqKOM, ENN, asj, wsHoMy, AQfA, gmjW, dkfnh, xiD, MKZ, BnR, AfV, ovjfW, rfP, iLDlI, HKzcS, Worqt, Using deep convolutional neural networks for volumetric Medical image segmentation model with convolutional! 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