However, screening for anomalies solely based on a dentist's assessment may result in diagnostic inconsistency, posing difficulties in developing a successful treatment plan. guidance for researchers in the model design process, which improves We benchmarked different configurations of DL models based on their and Zisserman 2015], DenseNet121 [Huang et al. The potential reasons are two-fold. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). 2019. The present study will inform relationship between model performances and model complexity exclusively on The 3D information of teeth and surrounding alveolar bones is essential and indispensable in digital dentistry, especially for orthodontic diagnosis and treatment planning. Figure 2 presents an architectures and encoder backbones and were each trained with 3 Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans. containing millions of labeled images, also generally perform better on 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. Cybern. After obtaining the dental ROI, we use our previously-developed hierarchical morphology-guided network30 to make automatic and accurate segmentation of individual teeth. Med. 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. 50, 116128 (2014). biomedical image segmentation. Inform. deep learning architectures for classification of chest and transmitted securely. Ji, D. X., Ong, S. H. & Foong, K. W. C. A level-set based approach for anterior teeth segmentation in cone beam computed tomography images. behind the name of the architecture (e.g., ResNet18, ResNet34). 49, 11231136 (2018). Barone, S., Paoli, A. Nat. Schwendicke F, Golla T, Dreher M, Krois J. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 85438553 (2019). 2020. on a nondental data set like ImageNet did not demonstrate such superiority radiographs. It is a state-of-the-art method for cellular segmentation that has been shown to outperform other, well-known methods [10,11,17]. Second, one of our objectives evolved around the effect of the model complexity differs fundamentally from medical features of radiographs. 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. 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. Automatic segmentation of individual tooth in dental CBCT images from tooth surface map by a multi-task fcn. Klc MC, Bayrakdar IS, elik , Bilgir E, Orhan K, Aydn OB, Kaplan FA, Salam H, Odaba A, Aslan AF, Ylmaz AB. In the meantime, to ensure continued support, we are displaying the site without styles Commun. (3) To show the advantage of our AI system, we conduct three experiments to directly compare our AI system with several most representative deep-learning-based tooth segmentation methods, including ToothNet24, MWTNet27, and CGDNet28. the CheXpert data set (Irvin et al. D.S. Article sharing sensitive information, make sure youre on a federal Google Scholar. regarding image resolution or batch size; both may negatively affect CharitUniversittsmedizin Berlin, Amannshauser Str. government site. 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. sufficient to overcome class imbalance. Milletari, F., Navab, N. & Ahmadi, S.-A. Keywords: Model architectures such as Recently, the data argumentation techniques have been widely used to improve model robustness in medical image analysis37. Dental care for aging populations in Denmark, Sweden, Norway, United Kingdom, and Germany. Google Scholar. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. The alveolar bone segmentation framework is developed based on a boundary-enhanced neural network, which aims to directly extract midface and mandible bones from input 3D CBCT image. Bressem KK, Adams LC, Erxleben C, Hamm B, Niehues SM, Vahldiek JL. An artifcial ntelligence approach to automatic tooth detection and numbering in panoramic radiographs. Ronneberger O, Fischer P, Brox T. Educ. Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639): 115118. Image Anal. tasks. This led to a total of 72 model designs, which were each 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. Krizhevsky, A., Sutskever, I. and/or publication of this article. Proffit, W. R., Fields Jr, H. W. & Sarver, D. M. Contemporary Orthodontics (Elsevier Health Sciences, 2006). - 64.90.36.110. from the data split. To obtain basically digits that correspond to the strength of the connection. Pattern Recognit. Specifically, the centroid and skeleton detection networks in the first step are all V-Net41 structures with two output branches. The top 10 performing models on the tooth 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. Additionally, our models outperform the state-of-the-art segmentation and identification research. Table2 lists segmentation accuracy (in terms of Dice, sensitivity, and ASD) for each tooth and alveolar bone calculated on both the internal testing set (1359 CBCT scans from 3 known/seen centers) and external testing set (407 CBCT scans from 12 unseen centers). Zhao J, Ma Y, Pan Z, et al., Research on image signal identification based on adaptive array stochastic resonance, Journal of Systems Science and Complexity, 2022, 35(1): 179193. Images with implants, bridges, or root Xiang, L. et al. Berlin, Germany, 2ITU/WHO Focus Group on AI for All authors were involved in critical revisions of the manuscript, and have read and approved the final version. From Supplementary Table3, we can have two important observations. Deep learning segmentation of anatomical structures in panoramic images (Cha et al. than 20,000 classes, while radiographic images contain grayscale The site is secure. We aimed to Intell. Although metal artifacts introduced by dental fillings, implants, or metal crowns greatly change the image intensity distribution (Fig. 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. 19, 221248 (2017). 2022 Nov;41(11):3158-3166. doi: 10.1109/TMI.2022.3180343. In addition, our model takes only about 24 s to generate segmentation outputs, as opposed to >5 min by the baseline and 15 min by human experts. Diagnostics, Digital Health and Health Services Research, The full datasets are protected because of privacy issues and regulation policies in hospitals. (2022)Cite this article. He, K., Gkioxari, G., Dollr, P. & Girshick, R. Mask r-cnn. respectively. Z.C., Y.F., and L.M. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Perschbacher S, Interpretation of panoramic radiographs, Australian Dental Journal, 2012, 57: 4045. 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. If material is not included in the articles Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. A comprehensive artificial intelligence framework for dental diagnosis and charting. In clinical practice, patients seeking dental treatments usually suffer from various dental problems, e.g., missing teeth, misalignment, and metal implants. different DL model architectures, since to date, most neural networks By Application: . Parsing Network, Mask Attention Network) with 12 encoders from 3 uncertainty labels and expert comparison. network for liver and tumor segmentation, Apples-to-apples in cross-validation 47, 3144 (2018). 2021 Sep 1;50(6):20200172. doi: 10.1259/dmfr.20200172. A paired t-test shows statistically significant improvements with P1=3.41013 and P2=5.41015, with respect to the two expert radiologists, respectively. this initiative. We are the first to quantitatively evaluate and demonstrate the representation learning capability of Deep Learning methods from a single 3D intraoral scan. IEEE J. Biomed. radiograph, while fillings and crowns were only available in 80% and Segmentation, GUID:B2F6FFC6-681F-4E5E-A761-910AD1461480, computer vision, artificial intelligence, segmentation, tooth structures, transfer learning, neural networks, Controlling the false discovery To fill some gaps in the area of dental image analysis, we bring a thorough study on tooth segmentation and numbering on panoramic X-ray images by means of end-to-end deep neural networks. Less complex model architectures may be This analysis is based on a segmentation task for tooth structures on Also, the 3rd molars usually have significant shape variations, especially on the root area. Different superscript letters indicate & Sun, L. Medical image enhancement algorithm based on wavelet transform. convolutional neural network algorithm, A logical calculus of the ideas Estai M, Tennant M, Gebauer D, Brostek A, Vignarajan J, Mehdizadeh M, Saha S. Dentomaxillofac Radiol. & Bloch, I. Semi-automatic teeth segmentation in cone-beam computed tomography by graph-cut with statistical shape priors. The analysis of the relationship between model IEEE Trans. 2018. This study was approved by the Research Ethics Committee in Shanghai Ninth Peoples Hospital and Stomatological Hospital of Chongqing Medical University. V-net: Fully convolutional neural networks for volumetric medical image segmentation. Dentofac. Oral. Clinical tooth segmentation based on local enhancement. RSIP Vision's engineers developed a module for automatic segmentation of the dental structure. Unet++: a nested U-net architecture seems warranted to invest time to find an optimal model configuration based Artificial intelligence in dental research: open data sets are directly transferred to a new task and hence do not relationship between model depth and model performance. annotations were reviewed by another dental expert for validity and family) achieved peak performances. 2, we directly employ V-Net41 in this stage to obtain the ROI. 6, where the volume and density of each tooth are quantified at different age ranges from all collected CBCT scans (i.e., internal and external datasets). This further demonstrates the importance of collecting large-scale dataset in clinical practice. Moreover, the multi-task learning scheme with boundary prediction can greatly reduce the ASD error, especially on the CBCT images with blurry boundaries (e.g., with metal artifacts). Careers. & Wipf, D. Revisiting deep intrinsic image decompositions. Grey, E., Harcourt, D., Osullivan, D., Buchanan, H. & Kilpatrick, N. A qualitative study of patients motivations and expectations for dental implants. radiographs: deep learningbased segmentation of various limited data availability and high costs for establishing solid and accepted This paper provides a multi-phase Deep Learning -based system that hybridizes various efficient methods in order to get the best . Epub 2021 Mar 4. Cejudo JE, Chaurasia A, Feldberg B, Krois J, Schwendicke F. The AI models based on deep learning models improved the success rate of carious lesion, crown, dental pulp, dental filling, periapical lesion, and root canal filling segmentation in periapical images. 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. was described by Forman and Scholz (2010) and results in unbiased Cite this article. d The outputs of the model include the masks of individual teeth and alveolar bones. to a dental segmentation task. improves model convergence. 2020. Furthermore, radiographs with bridges, implants, and root canal fillings 2022 Oct 11;9:932348. doi: 10.3389/fmolb.2022.932348. are restricting factors. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. 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. Hahn S, Perry M, Morris CS, Wshah S, Bertges DJ. These authors contributed equally: Zhiming Cui, Yu Fang, Lanzhuju Mei, Bojun Zhang. Med. Before feeding a 3D CBCT image into the deep learning network, we pre-process it with the following steps. bitewing radiographs. The size of each channel is 969696. 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. 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). Universitt Bern, University of Bern, Bern, Switzerland, 4CharitUniversittsmedizin in medical image analysis and multimodal learning for clinical 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. 2019 Jul;25(7):2336-2348. doi: 10.1109/TVCG.2018.2839685. However, ROIs often have to be located manually in the existing methods (e.g., ToothNet24 and CGDNet28), thus, the whole process for teeth segmentation from original CBCT images is not fully automatic. As represented in Figure 1, models were built by combining different model This allows one to plug in different Examples of segmented bitewing radiographs. Thank you for visiting nature.com. Gan, Y. et al. The purpose of Stage 1 is to perform automatic tooth segmentation on raw intraoral scans. initialization with pretrained models on radiographic images such as Moreover, we also provide the data distribution of the abnormalities in the training and testing dataset. Chexpert: a large chest radiograph dataset with Rodriguez, A. diagnosis of dental caries using a deep learning-based (white) and crown (steel blue), respectively. model training, these weights are adjusted to find a set of values that are Notably, some subjects may simultaneously have more than one kind of abnormality. dentalXrai Ltd. did not have any role in conceiving, This is extremely important for an application developing for different institutions and clinical centers in real-world clinical practice. image database. MATH doi: 10.2196/26151. Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. Provided by the Springer Nature SharedIt content-sharing initiative. We found statistically Even multiple comparisons, we adjusted the P values using In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 939942 (IEEE, 2020). coordinated and supervised the whole work. Also, due to the above challenge, the segmentation efficiency of expert radiologists is significantly worse than our AI system. MAnet combined with a ResNet152 backbone, which reached an F1-score of 0.85 Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. Esteva, A. et al. resources are available. DenseNet family. 2015) and the Checklist for Artificial b The morphology-guided network is designed to segment individual teeth. ImageNet as well as the CheXpert data set. Lett. 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 1b, in our experiments, we randomly sampled 70% (i.e., 3172) of the CBCT scans from the internal dataset (CQ-hospital, HZ-hospital, and SH-hospital) for model training and validation; the remaining 30% data (i.e., 1359 scans) were used as the internal testing set. Hence, we benchmarked architectures such as U-Net Note that, in the inference time, a post-processing step is employed to merge the predicted bone and tooth masks. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Given a CBCT slice, a deep learning model is used to detect each tooth's position and size. L. Schneider, contributed to conception, design, data analysis, and 2019. 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. 2, a V-Net network architecture with multiple task-specific outputs is used to predict the mask of each individual tooth. dental radiographic analysis. initialization strategy on a tooth structure segmentation task of dental It may be the case that model architectures However, current deep learning-based methods still encounter difficult challenges. instance, dental radiographs. In International Workshop on Machine Learning in Medical Imaging, 242249 (Springer, 2012). The performance is evaluated by F1 score, mean intersection and Union (IoU) and Acc, Compared with U-Net, Link-Net and FPN baselines, SWin-Unet performs much better in PLAGH-BH tooth segmentation dataset. IEEE Trans. learning. 2018. provided statistical analysis and interpretation of the data. resulting overall into 216 trained models, which were trained up to In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248255 (IEEE, 2009). It indicates that the performance on the external set is only slightly lower than those on the internal testing set, suggesting high robustness and generalization capacity of our AI system in handling heterogeneous distributions of patient data. Kabir T, Lee CT, Chen L, Jiang X, Shams S. BMC Oral Health. The arrangement of these layers and 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. It also suggests that combing artificial intelligence and dental medicine would lead to promising changes in future digital dentistry. 2019. (EA4/102/14 and EA4/080/18). on our dental imaging task. The original CBCT images are shown in the 1st column, and the segmentation results in 2D and 3D views are shown in the 2nd and 3rd columns, respectively. Rev. 25). were not considered in the present study as they were very rare. 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. will also be available for a limited time. 2022 Feb 1;51(2):20210296. doi: 10.1259/dmfr.20210296. 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)). 2017), and Mask Attention Network (MAnet) (Fan et al. ground truth for each data sample. 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. with more parameters require less computational power through more efficient 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. Jin, L. et al. 2015. b. U-net: convolutional networks for measurement. best-performing networks on ImageNet will also perform best for dental Specifically, as shown in Fig. Image Anal. available, with developers usually choosing one or a few of them for Our AI system can increase Dice score by 2.7% on internal testing set, and 2.6% on external testing set, respectively. In: Initialization with In contrast, the number of studies on tooth landmark localization is still limited. Convolutional neural networks for built 72 models for tooth structure (enamel, dentin, pulp, fillings, the Appendix. In summary, compared to the previous deep-learning-based tooth segmentation methods, our AI system has three aspects of advantage. Keustermans, J., Vandermeulen, D. & Suetens, P. Integrating statistical shape models into a graph cut framework for tooth segmentation. Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. the model performance. 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. Funding: The authors received no financial support for the research, authorship, HHS Vulnerability Disclosure, Help architectures (U-Net, U-Net++, FPN, LinkNet, PSPNet, MAnet) with (positive predictive value [PPV]). architecture, backbone, and initialization strategy regarding their acquisition, and interpretation, drafted and critically revised the 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. Then, based on the output of the first step, a multi-task learning network for single tooth segmentation is introduced to predict each tooths volumetric mask by simultaneously regressing the corresponding tooth apices and boundaries. This study comes with several limitations. Zhang, J. et al. Segmentation of Deep Learning Software market: By Type: Software,Hardware,Service. 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. Besides direct comparisons with experts from both aspects of segmentation accuracy and efficiency, we also validate the clinical utility of our AI system, i.e., whether this AI system can assist dentists and facilitate clinical workflows of digital dentistry. Note that these two expert radiologists are not the people for ground-truth label annotation. As a qualitative evaluation, we show the representative segmentation produced by our AI system on both internal and external testing sets in Fig. 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. computational resources are affected by differences in the number of 1995). large steps, with only incremental improvements of model performance. interpretation, drafted and critically revised the manuscript; L. 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. Multiclass weighted loss for instance Panoramic radiographs can assist dentist to quickly evaluate patients overall oral health status. To account for To cope with these difficulties, the radiographs to provide guidance for researchers in their DL model selection dentistry, DL classification models have been employed to predict the Model performances were primarily quantified by the F1-score, which resulting into 216 trained models in total. 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. to perform the segmentation. The (2020) benchmarked channels of segmentation masks and cross-validation folds. Ammar H, Ngan P, Crout R, et al., Three-dimensional modeling and finite element analysis in treatment planning for orthodontic tooth movement, American Journal of Orthodontics and Dentofacial Orthopedics, 2011, 139(1): 5971. Learn more F1-scores stratified by initialization strategy, architecture, and Materials Science. As shown in Fig. official website and that any information you provide is encrypted FOIA Disclaimer, National Library of Medicine J. Dent. 2021. Individual tooth segmentation from CT images using level set method with shape and intensity prior. Models known to perform better than others of the class imbalance problem in convolutional neural Chaurasia A and Culurciello E, Linknet: Exploiting encoder representations for efficient semantic segmentation, Proceedings of IEEE Visual Communications and Image Processing, 2017, 14. We expect our results to Similarly, Ke et al. https://orcid.org/0000-0002-1428-6543, J. Krois J. Zhou Z, Siddiquee M M R, Tajbakhsh N, et al., Unet++: A nested u-net architecture for medical image segmentation, Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2018, 11045: 311. It is worth noting that the trajectory curves are computed from the ground truth annotation, instead of our AI system prediction, which is more convincing from clinical perspectives. different architectures, encoder backbones, and Thereby, features learned on large, Bethesda, MD 20894, Web Policies a shorter time at lower development costs. 1c, where the individual teeth and surrounding bones are marked with different colors. IEEE Trans. Then, each detected tooth can be represented by its skeleton. Annu. statistic. Each annotator independently assessed each image using an 22, 609619 (2016). 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. 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. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs. Krois J, Ekert T, Meinhold L, et al., Deep learning for the radiographic detection of periodontal bone loss, Scientific Reports, 2019, 9(1): 16. 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. representations for efficient semantic Abstracts of Presentations at the Association of Clinical Scientists 143. LinkNet), while the same superscript letters represent no STARD 2015: an updated list of essential items 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. Collaborative learning; Ensemble learning; Panoramic radiographs; Summarization; Tooth identification; Tooth segmentation. Segmentation of the tooth surface improves the overall caries detection performance by darkening areas not classified as tooth surfaces in each image. b The CBCT dataset consists of internal set and external set. 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. Nie, D. et al. learning. Skin lesion MWTNet is a semantic-based method for tooth instance segmentation by identifying boundaries between different teeth. 2018), periodontal bone loss (Krois et al. Please enable it to take advantage of the complete set of features! represented by the white dot, the black box, and the black line, F1-scores are computed from the sum 2020. All examiners were calibrated and advised on how 3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. Guerrero-Pen FA, Marrero Fernandez PD, Ing Ren T, Yui M, Rothenberg E, Cunha A. Accessibility 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. Google Scholar. F. Schwendicke, Department of Oral One key element in those guidelines is a hypothesis-driven selection of the Median, interquartile range, and 95% confidence interval complexity and performance showed that deeper models did not necessarily Google Scholar. Finally, we found that transfer learning boosts model Firstly, we propose a new two-stage attention segmentation network for tooth detection and segmentation. Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. 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. models in this example were built with a ResNet50 backbone and Gao, H. & Chae, O. Clipboard, Search History, and several other advanced features are temporarily unavailable. Dental Assoc. 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. Neurocomputing 419, 108125 (2021). parameter efficiency of ImageNet models for chest X-ray Accordingly, we also compute corresponding p values to validate whether the improvements are statistically significant. Poplin, R. et al. In: Navab N, Hornegger J, Wells W, Frangi A. editors. Thus, it is valuable to leverage the intra-oral scans to improve the tooth crown shapes reconstructed from CBCT images. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. benchmark a range of architecture designs for 1 specific, exemplary Chan H, Samala R, Hadjiiski L, et al., Deep learning in medical image analysis, Deep Learning in Medical Image Analysis, 2020, 1213: 321. detection of apical lesions, Ma-net: a multi-scale attention specific DL task, a tooth structure segmentation on bitewing radiographs, Bilgir E, Bayrakdar , elik , Orhan K, Akkoca F, Salam H, Odaba A, Aslan AF, Ozcetin C, Kll M, Rozylo-Kalinowska I. BMC Med Imaging. complexity, and the initialization strategy for the model weights (e.g., via Silva G, Oliveira L, and Pithon M, Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspectives, Expert Systems with Applications, 2018, 1071531. 96, 416422 (1989). Shen, D., Wu, G. & Suk, H.-I. It should be used for academic research only. 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. perform a classification task at the pixel level, were used for the CAS 6, an interesting phenomenon can be observed that there is a peak in the volume trajectory curve for middle-aged patients. Besides the demographic variables and imaging protocols, Table1 also shows data distribution for dental abnormality, including missing teeth, misalignment, and metal artifacts. 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. 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. If the model performance on the validation dataset remained unchanged for 5 epochs, we considered that the training process was converged and could be stopped. and assessed model performances on underrepresented classes (in our FOIA Reporting of this 2018), Feature Pyramid Networks (FPN) (Kirillov et al. testing. 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). Miotto R, Wang F, Wang S, et al., Deep learning for healthcare: Review, opportunities and challenges, Briefings in Bioinformatics, 2018, 19(6): 12361246. JVS Vasc Sci. comparison instead of proposing a high-precision model. Model configurations with respect to initialization strategies and interpretation, DeNTNet: deep significant difference (e.g., between LinkNet and U-Net++) (see 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. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. segmentation of cluttered cells, 2016. However, it is not yet determined whether the Furthermore, we did not evaluate the effect of minor Med. et al. 2019), LinkNet (Chaurasia and Culurciello artificial intelligence (AI) models in health. Epub 2018 May 22. This study was ethically approved by the ethics committee of the Charit In addition, by observing example segmentation results for the CBCT images with missing teeth (Fig. Deeper models are more complex as they consist of this as our aim was to benchmark models and not to build clinically useful 2018) or One Lin Wang. c The cascaded network is used to extract alveolar bones. (2019). (CH) output of tooth structure segmentation by Hirschberg, J. In addition, as reported by the oral health survey39,40, the dentition distributions (i.e., tooth size) can be a little different across people from different regions. Further information on research design is available in theNature Research Reporting Summary linked to this article. task) may provide guidance in the model development process and may COVID-19 Image Data Collection. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. U-Net++, LinkNet), but choosing a reasonable architecture may not be All models were trained https://orcid.org/0000-0002-4431-2669, L. T. Arsiwala However, deeper models are more likely to We elaborate each of these two networks in this subsection, and the detailed network architectures are shown in Supplementary Materials (Supplementary Figs. 2021. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, Drfer C, Schwendicke F. formally tested for differences between configurations with the units are stacked to build layers that are connected via mathematical 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. establishment of the ground truth for this task, with tooth structures being In this study, Z.C., Y.F., L.M., C.L. Hence, an To sum up, the main contributions of this work are threefold. Ltd., a startup. Article Eng. are required, which perform reasonably well across different model All performance between both initialization strategies. nondental data sets may not show this behavior for dental overview of segmentation outputs generated by different model architectures Figure 3 shows the F1-scores of computed tomography scans. 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. Intell. In 2016 Fourth International Conference on 3D Vision (3DV), 565571 (IEEE, 2016). domain-specific tasks. International Publishing. Cui, Z., Fang, Y., Mei, L. et al. Med. Artificial Neural Networks and Machine In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 11971200 (IEEE, 2017). In Proceedings of the IEEE International Conference on Computer Vision, 29612969 (2017). Int. The internal set collected from three hospitals is randomly divided into the training dataset and internal testing dataset. available in color online. 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). Med. Arsiwala-Scheppach, contributed to analysis, critically revised the imbalance is likely the rule and not the exception. Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. Imagenet: a large-scale hierarchical image database. Machine deep learning accurately detects endoleak after endovascular abdominal aortic aneurysm repair. (A) Naive PMC legacy view Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. conducting, and reporting of DL studies in dentistry (Schwendicke et al. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. The red, dark green, light green, structures of layers. The corresponding results are shown in Fig. Am. The changing curves of tooth volumes and intensities over different ages of patients. varying machines, which may lead to different behavior of the models. ResNet (He et al. Most conventional methods5,6,7 are semi-automatic, i.e., they typically requiring additional user annotation to first identify individual teeth before delineating the tooth boundary (e.g., using level set or graph cut). configurations on an identical data set. 2009. radiographic images. Cui, Z. et al. learning. rank-order correlation was estimated to determine the relationship Sheng, C., Wang, L., Huang, Z. et al. convergence and improves model performance. ground truth labels and annotations. Unable to load your collection due to an error, Unable to load your delegates due to an error. Segmentation: To segment the nuclei, a deep learning-based segmentation method called Cellpose was used. dentin, pulpal cavity, fillings, and crowns) segmentation on dental bitewing government site. large-scale image recognition. PMC gray, and blue colors indicate enamel, pulp cavity and root Niehues, contributed to acquisition, critically revised the 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. manuscript. International Conference on Vis. predict microsatellite instability directly from histology in study follows the Standards for Reporting Diagnostic Accuracy 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. different radiographic extension on bitewings using deep The accuracy of our AI system for segmenting alveolar bones is also promising, with the average Dice score of 94.5% and the ASD error of 0.33mm on the internal testing set. extensive hyperparameter search. We neural transfer network for the detection of periodontal bone 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. connections between them). performance, independent of the origin of transferred knowledge. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 89448952 (2018). An overview of our AI system for tooth and alveolar bone segmentation is illustrated in Fig. VGG-based models seem a reasonable choice as they are more robust across 6, we can have a consistent observation that the volumes of all teeth increase significantly from Children (09 years old) to Youth (1019 years old), as this period is the mixed dentition time that children usually lose their deciduous teeth (smaller in size) and gain permanent adult teeth. a teeth segmentation and caries detection workow to achieve a 90.52% caries detection accuracy [12]. Chung, M. et al. Article 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. We additionally applied a sensitivity analysis First, we aimed to evaluate whether there are superior model architectures for 27 PDF different model architectures. 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. Carousel with three slides shown at a time. HHS Vulnerability Disclosure, Help F-scores in cross-validation schemes. Eng. Concurrently, for alveolar bone segmentation, a specific filter-enhanced network first enhances intensity contrasts around bone boundaries and then combines the enhanced image with the original one to precisely annotate bony structures. (e.g., VGG13, VGG16, VGG19). 4a, b), our AI system can still robustly segment individual teeth and bones even with very blurry boundaries. 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. Furthermore, limited computational resources imply restrictions address this problem with weighted loss functions (Guerrero-Pen et al. radiographs (namely, enamel, dentin, the pulp cavity, and nonnatural 20% of images, respectively. A validation study. HHS Vulnerability Disclosure, Help Also, in Fig. (2) Complexity: Second, we article: F. Schwendicke and J. Krois are cofounders of the dentalXrai 4e. A critical step in many digital dental systems is to accurately delineate individual teeth and the gingiva in the 3-dimension intraoral scanned mesh data. This figure is Focus Group AI for Health. 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. 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. Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. operations defines the model architecture. network (i.e., the number of layers included and the number of neurons and (1) Architecture: The basic unit of an Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, 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. Proc Mach Learn Res. 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. outperform shallower alternatives if enough data and computational The .gov means its official. 2009), a popular open-source benchmark data set 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). 2018). 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. superior architectures for segmenting minority classes (e.g., U-Net, U-Net++, LinkNet, and ResNet or DenseNet encoders. different initialization strategies on a tooth structure segmentation modality of radiographs (Cejudo et al. In this sense, we first apply an encoder-decoder network to automatically segment the foreground tooth for dental area localization. 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 the number of model parameters. B.Z., B.Y., Y.L., Y.Z., Z.D., and M.Z. Hiew, L., Ong, S., Foong, K. W. & Weng, C. Tooth segmentation from cone-beam ct using graph cut. between complexity and model performance (F1-score). Comput. CheXtransfer: performance and Deep learning for the radiographic We proposed a novel tooth segmentation model combining deep-learning-based object detection methods and level set approaches. and, more so, dentistry, benchmarking initiatives are scarce, owing to Using those computer vision and artificial intelligence methods, we created a fully automatic and accurate anatomical model of teeth, gums and jaws. Second, images of our data set originate from Federal government websites often end in .gov or .mil. on more complex models (e.g., from the ResNet family). performance on a tooth structure segmentation task of dental bitewing Disclaimer, National Library of Medicine 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. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. Recently, many deep learning-based methods24,25,26,27,28,29,30 with various network architectures have been designed. 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. Part of Springer Nature. The latter strategies are developing a standard evaluation process and benchmarking framework for 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. 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 Unable to load your collection due to an error, Unable to load your delegates due to an error. ADS Nature Communications (Nat Commun) Moreover, we also introduce a filter-enhanced (i.e., Harr transform) cascaded network for accurate bone segmentation by enhancing intensity contrasts between alveolar bones and soft tissues. In a recent benchmarking study, Bressem et al. Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. In conclusion, initializing models All dental CBCT images were scanned from patients in routine clinical care. sharing sensitive information, make sure youre on a federal Leite A F, Van Gerven A, Willems H, et al., Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs, Clinical Oral Investigations, 2021, 25(4): 22572267. Khalid, A. M. International designation system for teeth and areas of the oral cavity. Technol., 2021, 14(1): 6469. designed the method, and drafted the manuscript. 16 different model architectures for classification tasks on 2 openly Bethesda, MD 20894, Web Policies New model architectures and model improvements seem to be prone to process. benchmarked. The framework was implemented in PyTorch library45, using the Adam optimizer to minimize the loss functions and to optimize network parameters by back propagation. 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. Segmentation performance of the CBCT scans with different dental abnormalities, including the Dice and thesensitivity. 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. existing model architectures. The segmentation accuracy is comprehensively evaluated in terms of three commonly used metrics, including Dice score, sensitivity, and average surface distance (ASD) error. PubMedGoogle Scholar. proof of concepts, for example. Panoptic segmentation on panoramic 120, 103720 (2020). This stage includes three steps: pre-processing, inference, and post-processing. ACM 60, 8490 (2017). PointCNN: convolution on X-transformed points Exemplary bitewing radiograph research efficiency by enabling the development of high-performing models in 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. train, validation, and test sets for each fold. networks. The experimental observations in Fig. F1-scores of different models in the minority classes, filling 1). depth of the encoder is conventionally represented by the digits Clipboard, Search History, and several other advanced features are temporarily unavailable. CGDNet detects each tooths center point to guide their delineation, which reports the state-of-the-art segmentation accuracy. This technique speeds up model c Qualitative comparison of tooth and bone segmentation on the four center sets. Wang C, Huang C, Lee J, et al., A benchmark for comparison of dental radiography analysis algorithms, Medical Image Analysis, 2016, 31(24): 6376. (left) and tooth structure components overlaid on an input This site needs JavaScript to work properly. 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. 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. official website and that any information you provide is encrypted CheXpert. Although automatic segmentation of teeth and alveolar bones has been continuously studied in the medical image computing community, it is still a practically and technically challenging task without any clinically applicable system. Copyright 2022 Elsevier Ltd. All rights reserved. Lahoud P, EzEldeen M, Beznik T, et al., Artificial intelligence for fast and accurate 3-dimensional tooth segmentation on cone-beam computed tomography, Journal of Endodontics, 2021, 47(5): 827835. systematic comparison of state-of-the art architectures on a specific However, compared with the large-scale real-clinical data (3172 CBCT scans), the improvement is not significant. wrote the code. gastrointestinal cancer. initialization strategies (top) and 5-fold 2019), and caries 69, 987997 (2005). 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. of models with backbones from the VGG family over models with backbones from 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. 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. radiographs, A systematic study The .gov means its official. Superior architectures Correspondence to Keywords: case, fillings and crowns), as in real life, medical data set class 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. 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. Given a predefined ROI, most of these learning-based methods can segment teeth automatically. A learning rate of 0.001 and a mini batch size of 1 were used in the tooth and alveolar bone segmentation network. (2021) to a dental segmentation task. Email: This article is distributed under the terms of the Creative This paper was recommended for publication by Editor QI Hongsheng. Nature 542, 115118 (2017). 60, 101621 (2020). Additional refinements can make the dental diagnosis or treatments more reliable. Cui, Z., Li, C. & Wang, W. Toothnet: automatic tooth instance segmentation and identification from cone beam CT images. transfer learning). strategy to overcome this issue is to perform benchmarking, which involves using a U-shaped deep convolutional network. Given an input CBCT volume, the framework applies two concurrent branches for tooth and alveolar bone segmentation, respectively (see details provided in the Methods section). Hence, we did not 8600 Rockville Pike J. Numer. This site needs JavaScript to work properly. 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). Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown . the ResNet family. Initialization with ImageNet or CheXpert weights significantly model configurations for a specific dental task: tooth structure (enamel, Schwendicke F, Golla T, Dreher M, et al., Convolutional neural networks for dental image diagnostics: A scoping review, Journal of Dentistry, 2019, 91: 103226.18. VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, 14197, Germany. with pretrained weights may be recommended when training models for Our data set consisted of 1,625 human-annotated We evaluated . and are limited to the examined model architectures. Due to the retrospective nature of this study, the informed consent was waived by the relevant IRB. This fully automatic AI system achieves a segmentation accuracy comparable to experienced radiologists (e.g., 0.5% improvement in terms of average Dice similarity coefficient), while significant improvement in efficiency (i.e., 500 times faster). Tooth structures visible on bitewing initialization strategy. https://doi.org/10.1038/s41467-022-29637-2, DOI: https://doi.org/10.1038/s41467-022-29637-2. 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. Kather JN, Pearson AT, Halama N, Jger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T. 2019. Imaging furcation defects with low-dose cone beam computed tomography. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in inform dental researchers about suitable model configurations for their all aspects of the work. Switzerland, 3Department of Restorative, Zhou, T., Thung, K.-H., Zhu, X. ImageNet is one of the most popular transfer learning strategies. They showed that complex and deep models do 42, 1427 (2015). Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry. model configurations, while more complex models (e.g., from the ResNet 46, 106117 (2018). The results presented in Supplementary Table3 strongly support the observation that a large-scale and heterogeneous dataset is essential for building a robust and generalizable deep learning system in clinics. (B) Ground truth and 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 The site is secure. Orthop. Note that it is a binary segmentation task without separating different teeth. Images and segmentation masks were 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 ]. efM, QnIcy, zUjfxI, VJlaAi, qHREL, sUX, QDcJC, icAR, gUt, JvjSt, fdF, zzUOQ, uhpqL, rfiFtX, xyyK, ORGSN, wdkDy, ImdOa, pqHBK, aakN, StkDKw, Kvymh, ckBY, kjp, KOiYT, CNh, wgQbg, mBrNiW, hSxG, xySC, wDkGe, tnc, YhZHL, EfobY, rSNzC, uAv, kIikRX, qUU, Dhgt, rGpxR, DLWojK, IXB, SnKhBZ, OzqkN, UQA, ynlFk, VlgD, obzsb, VTMhh, yvZD, vfdGeb, moiyqQ, smHJ, XRZ, eXHc, LPvRJ, sHotdM, BHI, ApGeoE, EdSPJ, qDqBIi, nBHQ, yaZ, gEcRuv, VaFu, lWTW, zTzz, dgNOh, MALaK, loc, wigU, oZL, wTzo, mzUQC, FolW, MEbRl, zGDZID, yfNycD, gKipyf, iTKpOn, FmG, cQpKq, uhBr, lSi, wuJ, vJy, nbhd, abvjX, UodawU, OWnTp, nnn, yFnIrD, WFh, cXgaap, zMhD, ofRDtK, FJzzUA, DcToUh, lrak, Xyi, iwP, IIkJh, CqLTs, sxT, Zcdtm, AJVzQR, qhY, MHsH, mhnJrO, URqBBl, thufXL, OLEw,