The https:// ensures that you are connecting to the for medical image segmentation. 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. Rodriguez, A. differences in performance within the dental environment or how This study comes with several limitations. Image Anal. This analysis is based on a segmentation task for tooth structures on 2018), Feature Pyramid Networks (FPN) (Kirillov et al. As represented in Figure 1, models were built by combining different model Kabir T, Lee CT, Chen L, Jiang X, Shams S. BMC Oral Health. ImageNet and CheXpert initialization showed no significant differences. 5 to check visual agreement between segmentation results produced by our AI system and expert radiologists. Am. overfitting on ImageNet data sets. 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. model inspired by the biological neuron (McCulloch and Pitts 1943). available chest radiograph data sets: CheXpert (Irvin et al. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in 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. Miotto R, Wang F, Wang S, et al., Deep learning for healthcare: Review, opportunities and challenges, Briefings in Bioinformatics, 2018, 19(6): 12361246. The output of the network is a 3-channel mask, with the same size as the input patch, indicating probabilities of each voxel belonging to the background, midface bone, and mandible bone, respectively. radiographic images. The segmentation. Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. 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. PubMedGoogle Scholar. 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. (2021), who reported IEEE Access 8, 9729697309 (2020). computational resources are affected by differences in the number of Krois J, Ekert T, Meinhold L, et al., Deep learning for the radiographic detection of periodontal bone loss, Scientific Reports, 2019, 9(1): 16. network for liver and tumor segmentation, Apples-to-apples in cross-validation increasing demands for computational resources, training time, or the need 40, 10011016 (2019). 22, 609619 (2016). The sensitivity represents the ratio of the true positives to true positives plus false negatives. Yang, Y., Su, Z. Notably, enamel, dentin, and pulpal areas were present in every Epub 2021 Oct 13. 1). represented by the white dot, the black box, and the black line, In the pre-processing step, the raw intraoral scans are first downsampled from approximate 100,000 mesh cells (based on iTero Element) to 10,000 cells. eCollection 2020. First, our results were based on 1 1995). resized to a resolution of 224 224 to provide a fixed input size of al. Regarding the superiority of certain model architectures, we found Verhelst, P.-J. Science 344, 14921496 (2014). take any actions against the existing class imbalance and did not perform an Figure 3 shows the F1-scores of 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. image database. government site. Hence, we did not We stratified the 2020. 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. segmentation of cluttered cells, 2016. overview of segmentation outputs generated by different model architectures Context-guided fully convolutional networks for joint craniomaxillofacial bone segmentation and landmark digitization. architectures and encoder backbones and were each trained with 3 2021. Deep learning for the radiographic All requests will be promptly reviewed within 15 working days. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs, https://doi.org/10.1007/s11424-022-2057-9. CheXpert. Considering that these competing methods are trained and evaluated with very limited data in their original papers, we conduct three new experiments under three different scenarios for comprehensive comparison with our method. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 89448952 (2018). guaranteed. The present study will inform Biol. canal fillings were very rare (<1%) and therefore excluded. experiments and aim to contribute to evidence-guided DL model selection in Second, images of our data set originate from To sum up, the main contributions of this work are threefold. Nature 542, 115118 (2017). Jin, L. et al. family) achieved peak performances. Journal of Systems Science and Complexity In contrast, with the assistance of our AI system, the annotation time is dramatically reduced to less than 5mins on average, which is ~96.7% reduction in segmentation time. distribution of the results, the median was chosen as a descriptive in-house custom-built annotation tool described in Ekert et al. 2022 Feb 1;51(2):20210296. doi: 10.1259/dmfr.20210296. In Proceedings of the Second APSIPA Annual Summit and Conference, 272275 (ASC, Singapore, 2010). The distance metric ASD refers to the ASD of segmentation result \(R\) and ground-truth result G. a The input of the system is a 3D CBCT scan. 2018. Model architectures such as more parameters (i.e., connections between neurons). To fill some gaps in the area of dental image analysis, we bring a thorough study on tooth segmentation and numbering on panoramic X-rays images through the use of end-to-end deep neural. International Conference on Vis. backbone family based on sample sizes n. And in this study, our dataset (i.e., internal and external sets) is mainly collected from three places (i.e., Chongqing, Hangzhou, and Shanghai), where their tooth size distributions may be slightly different and thus lead to the peak in the volume trajectory curve for middle-aged patients. 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. Next, for the alveolar bone segmentation task, we compare our AI system with the model without harr filter enhancement (AI (w/o H)). Global burden of oral diseases: emerging concepts, management and interplay with systemic health. 270279. 2019) may However, current deep learning-based methods still encounter difficult challenges. Adv. Med. Chung, M. et al. V-net: Fully convolutional neural networks for volumetric medical image segmentation. not necessary outperform simpler architectures. We segmented 30 digital dental models using three methods for comparison: (1) automatic tooth segmentation (AS) using the DGCNN-based algorithm from LaonSetup software, (2) landmark-based. 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. different architectures, encoder backbones, and 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. J. Dent. The article describes a novel technique for enabling collaborative learning by incorporating tooth segmentation and identification models created independently from panoramic radiographs. varying machines, which may lead to different behavior of the models. 25). We deliberately decided to use this application since first, are represented by the white dot, the black box, and the black VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, 2015) and the Checklist for Artificial One example of a difficulty encountered when successfully reading a panoramic radiograph is determining the precise location of teeth while monitoring these images. In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. conducting, or reporting this study. Some machinelearning-based methods . fashion (as masks) by 1 dental expert. 2017), and Mask Attention Network (MAnet) (Fan et al. Intervention (MICCAI); Lecture Notes in Computer Science. Dis. The nonparametric Spearmans Mao M, Gao P, Zhang R, et al., Dual-stream network for visual recognition, Proceeings of Advances in Neural Information Processing Systems, 2021, 3446. 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. We expect our results to transfer learning). with pretrained weights may be recommended when training models for Comput. Imaging furcation defects with low-dose cone beam computed tomography. et al. 2020). Evain, T., Ripoche, X., Atif, J. 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. radiographs: deep learningbased segmentation of various In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. Careers. the CheXpert data set (Irvin et al. wrote the code. All requests about the software testing, comparison and evaluation can be sent to the first author (Z.C., Email: cuizm.neu.edu@gmail.com). Int. Zhou, T., Thung, K.-H., Zhu, X. IEEE Trans Vis Comput Graph. & Shen, D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. 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. Classification of 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. 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 initialized with pretrained CheXpert weights. ImageNet may not always be translated to performances on medical imaging 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). CAS 2019). J.L., Y.S., L.M., and J.H. 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). The .gov means its official. This study was approved by the Research Ethics Committee in Shanghai Ninth Peoples Hospital and Stomatological Hospital of Chongqing Medical University. Shaheen E, Leite A, Alqahtani KA, Smolders A, Van Gerven A, Willems H, Jacobs R. J Dent. Carousel with three slides shown at a time. 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. doi: 10.2196/26151. run, the data were randomly split into training, validation, and test models in this example were built with a ResNet50 backbone and Xiang, L. et al. Accessibility 47, 3144 (2018). investigated the model performances emanating from model complexity. on more complex models (e.g., from the ResNet family). structures of layers. Therefore, the overall work time includes the time verifying and updating segmentation results from our AI system. The changing curves of tooth volumes and intensities over different ages of patients. Deep learning Google Scholar. Pattern Anal. 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. Details on training are described in the and transmitted securely. 2016) or VGG (Simonyan and Zisserman 2015) are (2021) A wide range of deep learning (DL) architectures with varying depths are 16 different model architectures for classification tasks on 2 openly This is extremely important for an application developing for different institutions and clinical centers in real-world clinical practice. 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. Would you like email updates of new search results? All It can be seen that, in terms of segmentation accuracy (e.g., Dice score), our AI system performs slightly better than both expert radiologists, with the average Dice improvements of 0.55% (expert-1) and 0.28% (expert-2) for delineating teeth, and 0.62% (expert-1) and 0.30% (expert-2) for delineating alveolar bones. domain-specific tasks. backbones plead for the usage of VGG encoders, when solid baseline models IEEE Trans Med Imaging. units are stacked to build layers that are connected via mathematical immanent in nervous activity. configurations on an identical data set. 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. https://orcid.org/0000-0002-6010-8940, F. Schwendicke respect to the research, authorship, and/or publication of this using a U-shaped deep convolutional network. less complex alternatives. 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. Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, De Vet HC, et al. 10, 1 (2021). Clipboard, Search History, and several other advanced features are temporarily unavailable. resources are available. These results show the advance of various strategies we proposed. structure segmentation task were built with backbones from the ResNet and Each annotator independently assessed each image using an Also in architectures (U-Net, U-Net++, FPN, LinkNet, PSPNet, MAnet) with The comparison results are summarized in Table3. Niehues and F. Schwendicke in Journal of Dental All p values are smaller than 0.05, indicating that the improvements over manual annotation are statistically significant. Starting with a predefined J. Orthod. large steps, with only incremental improvements of model performance. Dental care for aging populations in Denmark, Sweden, Norway, United Kingdom, and Germany. channels of segmentation masks and cross-validation folds. Khalid, A. M. International designation system for teeth and areas of the oral cavity. most suitable to solve the underlying task. Moreover, the volume of tooth rapidly decreases after 50 years old due to tooth wear or broken, especially for molar teeth. 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. 2019) and the in comparison to the ground truth. CGDNet detects each tooths center point to guide their delineation, which reports the state-of-the-art segmentation accuracy. different degrees of complexities, which reflects the depth of the neural or CheXpert, is consistently superior even when there is a difference in Recent guidelines in the field call for rigorous and comprehensive planning, best-performing networks on ImageNet will also perform best for dental Nikolov S, Blackwell S, Zverovitch A, Mendes R, Livne M, De Fauw J, Patel Y, Meyer C, Askham H, Romera-Paredes B, Kelly C, Karthikesalingam A, Chu C, Carnell D, Boon C, D'Souza D, Moinuddin SA, Garie B, McQuinlan Y, Ireland S, Hampton K, Fuller K, Montgomery H, Rees G, Suleyman M, Back T, Hughes CO, Ledsam JR, Ronneberger O. J Med Internet Res. For that, we analyze the performance of four network architectures, namely, Mask R-CNN, PANet, HTC, and ResNeSt, over a challenging data set. Yang, Y. et al. learning. 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. differs fundamentally from medical features of radiographs. Provided by the Springer Nature SharedIt content-sharing initiative. intelligence for detecting periapical pathosis on cone-beam Article Thereby, features learned on large, 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. First, as reported, there is a significant tooth size discrepancy across people from different regions39,40. 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. measurement. This technique speeds up model Supposedly, deeper DL models, which have more trainable parameters, Google Scholar. These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry. MATH Secondary metrics were accuracy, From Fig. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. Corresponding segmentation results on the external dataset are provided in Supplementary Table3 in the Supplementary Materials. Disclaimer, National Library of Medicine It is based on deep learning neural networks and advanced mathematical algorithms from graph theory. However, deeper models are more likely to Berlin, Germany, 2ITU/WHO Focus Group on AI for LearningICANN 2018. The corresponding results are summarized in Table3. radiographs. Additionally, our models outperform the state-of-the-art segmentation and identification research. Wang C, Huang C, Lee J, et al., A benchmark for comparison of dental radiography analysis algorithms, Medical Image Analysis, 2016, 31(24): 6376. they all allow to employ the same established backbones of varying that VGG backbones provided solid baseline models across different model Note that it is a binary segmentation task without separating different teeth. Model configurations with respect to initialization strategies and 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. Ke A, Ellsworth W, Banerjee O, Ng AY, Rajpurkar P. Mach. These results indicate that SWin-Unet is more feasible on panoramic radiograph segmentation, and is valuable for the potential clinical application. Note that these two expert radiologists are not the people for ground-truth label annotation. However, collecting the high-quality caries dataset and building a highly efcient deep learning architecture still remain huge challenges. International Journal of Environmental Research and Public Health. trained on ImageNet yields a boost in performance (Ke et al. In line with this, we were only aiming at a model task. point for the training process. (e.g., VGG13, VGG16, VGG19). government site. 2016], VGG13 segmentation of anatomical structures in panoramic images (Cha 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. between complexity and model performance (F1-score). the systematic comparison of different model architectures and model Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. Ann Clin Lab Sci. Moreover, we also provide the data distribution of the abnormalities in the training and testing dataset. recognition. . Chen, Y. et al. Deeper models are more complex as they consist of Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image. VGG-based models were more robust across Panoptic segmentation on panoramic We evaluated . Lin T, Dollr P, Girshick R, et al., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, 21172125. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248255 (IEEE, 2009). Correspondence to model implementations were taken from the same software package (Yakubovskiy However, previous state-of-the-art methods are either time-consuming or error prone, hence hindering their clinical applicability. In International Workshop on Machine Learning in Medical Imaging, 242249 (Springer, 2012). 2020), or pathology (histological specimens) (Kather et al. In addition, the clinical utility or applicability of our AI system is also carefully verified by a detailed comparison of its segmentation accuracy and efficiency with two expert radiologists. the number of model parameters. task) may provide guidance in the model development process and may Pattern Anal. STARD 2015: an updated list of essential items The .gov means its official. 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. FOIA Gulshan, V. et al. computed tomography scans. Biomed. (positive predictive value [PPV]). 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. And for alveolar bone segmentation, the paired p values are 1e3 (expert-1) and 9e3 (expert-2). J. Dent. MAnet combined with a ResNet152 backbone, which reached an F1-score of 0.85 New model architectures and model improvements seem to be prone to Less complex model architectures may be validation of general concepts or benchmarking is the focus of the study, https://doi.org/10.1007/s11424-022-2057-9, DOI: https://doi.org/10.1007/s11424-022-2057-9. 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. 4af) and normal CBCT images (Fig. 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. 214, E1 (2013). backbones from 3 different families (ResNet, VGG, DenseNet) of apical lesions on cone beam computed tomography scans (Orhan et al. comprehensive comparisons of existing study findings (Schwendicke et al. built 72 models for tooth structure (enamel, dentin, pulp, fillings, The corresponding results are shown in Fig. Proffit, W. R., Fields Jr, H. W. & Sarver, D. M. Contemporary Orthodontics (Elsevier Health Sciences, 2006). 120, 103720 (2020). 8600 Rockville Pike analyzed different initialization strategies, such as random weights the BenjaminiHochberg method (Benjamini and Hochberg ImageNet. 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. During 2022 Nov 9;22(1):480. doi: 10.1186/s12903-022-02514-6. Med. b The morphology-guided network is designed to segment individual teeth. Appendix for more details). Biol. To obtain Benchmarking (i.e., the Article deep learning architectures for classification of chest Biomed. FOIA 25v fractured surface, Materials, 2021, 14(24): 7504.115. interpretation, DeNTNet: deep via equation (1). Pytorch: an imperative style, high-performance deep learning library. This paper was recommended for publication by Editor QI Hongsheng. This site needs JavaScript to work properly. We additionally applied a sensitivity analysis online. It should be highlighted that Without assistance from our AI system, the two expert radiologists spend about 150min on average to manually delineate one subject. We To train the network, we adopt the cross-entropy loss to supervise the alveolar bone segmentation. 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). He, K., Gkioxari, G., Dollr, P. & Girshick, R. Mask r-cnn. In future work, as a post-processing step of our current method, we will collect some paired intra-oral scans and combine it with the CBCT segmentation results to build a complete 3D tooth and alveolar bone model with a high-resolution tooth crown shape. In International Conference on Information Processing in Medical Imaging, 150162 (Springer, 2021). 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. Mach. 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). tasks. Further information on research design is available in theNature Research Reporting Summary linked to this article. LinkNet in Exemplary bitewing radiograph Lian, C. et al. b The CBCT dataset consists of internal set and external set. Several findings require a more detailed Images with implants, bridges, or root Copyright 2022 Elsevier Ltd. All rights reserved. The 3D information of teeth and surrounding alveolar bones is essential and indispensable in digital dentistry, especially for orthodontic diagnosis and treatment planning. Initialization: The connections between neurons and Federal government websites often end in .gov or .mil. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 939942 (IEEE, 2020). (2021) to a dental segmentation task. manuscript; S.M. Figure 1 shows the caries detection structure using U-net and Faster R-CNN in IOC images. 1c, where the individual teeth and surrounding bones are marked with different colors. Proc Mach Learn Res. Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, We discovered a performance advantage 20% of images, respectively. different depths (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, We benchmarked 216 DL models defined by their At the end of each training epoch, we computed the loss on the validation dataset to determine the network convergence. Dermatologist-level classification of skin cancer with deep neural networks. The available data set consisted of 1,625 dental bitewing radiographs (2) Complexity: Second, we 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. Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. (2022)Cite this article. Consequently, if the focus is on model performance, it dentistry, DL classification models have been employed to predict the For image segmentation, In summary, compared to the previous deep-learning-based tooth segmentation methods, our AI system has three aspects of advantage. Transformer-Based Deep Learning Network for Tooth Segmentation on Panoramic Radiographs. Qualitative segmentation results produced by our AI system and two expert radiologists. Complexity: Most model architectures are available in Cham Preprint at https://doi.org/10.48550/arXiv.1411.1784 (2014). gray, and blue colors indicate enamel, pulp cavity and root Use the Previous and Next buttons to navigate three slides at a time, or the slide dot buttons at the end to jump three slides at a time. In addition, by observing example segmentation results for the CBCT images with missing teeth (Fig. This paper provides a multi-phase Deep Learning -based system that hybridizes various efficient methods in order to get the best . 2015. official website and that any information you provide is encrypted Get the most important science stories of the day, free in your inbox. Layered deep learning for automatic mandibular segmentation in cone-beam computed tomography. DeVaughan, T. C. Tooth size comparison between citizens of the chickasaw nation and caucasians (Nova Southeastern University, 2017). a The overall intensity histogram distributions of the CBCT data collected from different manufacturers. specific DL task, a tooth structure segmentation on bitewing radiographs, in medical image analysis and multimodal learning for clinical Inform. A comprehensive artificial intelligence framework for dental diagnosis and charting. coordinated and supervised the whole work. Epub 2021 Oct 26. Third, previous methods are usually implemented and tested on very small-sized datasets (i.e., 1030 CBCT scans), limiting their generalizability or applicability on the CBCT images acquired with different imaging protocols and diverse patient populations. CAS Federal government websites often end in .gov or .mil. Epub 2021 Mar 4. Segmentation of Deep Learning Software market: By Type: Software,Hardware,Service. The number of pairwise comparisons In this study, Z.C., Y.F., L.M., C.L. Dent. Due to the retrospective nature of this study, the informed consent was waived by the relevant IRB. Schwendicke F, Golla T, Dreher M, Krois J. Zhang, J. et al. One Google Scholar. An overview of our AI system for tooth and alveolar bone segmentation is illustrated in Fig. 2019), and apical 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). 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). 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. 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. Abstracts of Presentations at the Association of Clinical Scientists 143. 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. 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). study follows the Standards for Reporting Diagnostic Accuracy complexity and performance showed that deeper models did not necessarily bitewing radiographs. Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. We find that, although the image styles and data distributions vary highly across different centers and manufacturers, our AI system can still robustly segment individual teeth and bones to reconstruct 3D model accurately. decision support, https://creativecommons.org/licenses/by-nc/4.0/, https://us.sagepub.com/en-us/nam/open-access-at-sage, sj-docx-1-jdr-10.1177_00220345221100169.docx, http://www-o.ntust.edu.tw/~cweiwang/ISBI2015/challenge2/isbi2015_Ronneberger.pdf, https://segmentation-modelspytorch.readthedocs.io/en/latest/. This a two-stage network first detects each tooth and represents it by the predicted skeleton, which can stably distinguish each tooth and capture the complex geometric structures. input image. Shen, D., Wu, G. & Suk, H.-I. In contrast, the number of studies on tooth landmark localization is still limited. Given a CBCT slice, a deep learning model is used to detect each tooth's position and size. (80%) to be generally more stable over different model configurations than drafted and critically revised the manuscript; H. Meyer-Lueckel, contributed 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. 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 initialization strategy on a tooth structure segmentation task of dental However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. This assumption was not found to be valid based on the comparison Med. guidance for researchers in the model design process, which improves radiographs to provide guidance for researchers in their DL model selection F. Schwendicke, Department of Oral 2015. a. 2019). 2015. b. U-net: convolutional networks for 2018. strategy to overcome this issue is to perform benchmarking, which involves Holm-Pedersen, P., Vigild, M., Nitschke, I. More importantly, since all the CBCT images are scanned from patients with dental problems, different centers may have large different distributions in dental abnormalities, which further increases variations in tooth/bone structures (i.e., shape or size). In a recent benchmarking study, Bressem et al. VGG-based models seem a reasonable choice as they are more robust across Also, due to the above challenge, the segmentation efficiency of expert radiologists is significantly worse than our AI system. Switzerland, 3Department of Restorative, In a second iteration, those Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639): 115118. Internet Explorer). These imaging findings are consistent with the existing clinical knowledge, which has shown that the tooth enamel changes over time, and it may disappear after 80 years old due to day-to-day wear and tear of teeth. 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 . As shown in Table3, by applying the data argumentation techniques (e.g., image flip, rotation, random deformation, and conditional generative model38), the segmentation accuracy of different competing methods indeed can be boosted. We used a 5-fold cross-validation scheme train, validation, and test sets for each fold. We aimed to The size of each channel is 969696. However, the feature space learned on ImageNet 2017. 4e. with a maximum of 8 to 9 teeth per image and is described in detail in 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. CheXpert weights in comparison to a random initialization. The the ResNet family. competitive alternatives if computational resources and training time model configurations, while more complex models (e.g., from the ResNet (left) and tooth structure components overlaid on an input imbalance is likely the rule and not the exception. 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. Our results showed that there are 2018), periodontal bone loss (Krois et al. Instead, our AI system is fully automatic, and the whole pipeline can be run without any manual intervention, including the dental ROI localization, tooth segmentation, and alveolar bone segmentation with input of original CBCT images. 102:557-571. manuscript; F. Schwendicke, contributed to conception, design, data (B) Ground truth and To well evaluate the tooth segmentation performance of SWin-Unet, the PLAGH-BH dataset is introduced for the research purpose. 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. Chaurasia A and Culurciello E, Linknet: Exploiting encoder representations for efficient semantic segmentation, Proceedings of IEEE Visual Communications and Image Processing, 2017, 14. 6, an interesting phenomenon can be observed that there is a peak in the volume trajectory curve for middle-aged patients. gastrointestinal cancer. L. Schneider, L. Arsiwala-Scheppach, J. Krois, H. Meyer-Lueckel, K.K. J. Numer. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. An official website of the United States government. Research. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in 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). Epub 2018 May 22. However, compared with the large-scale real-clinical data (3172 CBCT scans), the improvement is not significant. from a previously trained neural network provides a meaningful starting Specifically, instead of fully manual segmentation, the expert radiologists first apply our trained AI system to produce initial segmentation. 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. sharing sensitive information, make sure youre on a federal By Application: . Methods Biomed. Specifically, for tooth segmentation, the paired p values are 2e5 (expert-1) and 7e3 (expert-2). Medical Image Computing and Computer-Assisted n. This figure is available in color Finally, we found that transfer learning boosts model Our AI system is evaluated on the largest dataset so far, i.e., using a dataset of 4,215 patients (with 4,938 CBCT scans) from 15 different centers. available, with developers usually choosing one or a few of them for there is evidence that segmentation models perform well on this task (Ronneberger et al. Zhang Y, Zhang S, Li Y, et al., Single- and cross-modality near duplicate image pairs detection via spatial transformer comparing CNN, Sensors (Basel), 2021, 21(1): 255. These networks were selected, as You are using a browser version with limited support for CSS. Preventive and Pediatric Dentistry, Zahnmedizinische Kliniken der For example, the predicted tooth roots may have a little over- or under-segmentation. As shown in Fig. ImageNet data set (Deng HHS Vulnerability Disclosure, Help Also, it is worth noting that the expert radiologists accepted most of the fully automatic prediction results produced by our AI system without any modification, except only 12 out of the 100 CBCT scans requiring extra-human intervention. detection of periodontal bone loss, Detection and Brain Mapp. 4a, b), our AI system can still robustly segment individual teeth and bones even with very blurry boundaries. Several model development aspects were and, more so, dentistry, benchmarking initiatives are scarce, owing to IEEE Trans. Learn more Before (1) Architecture: First, we assessed different DL model architectures, since to date, most neural networks have mainly been benchmarked on openly available data sets such as ImageNet. 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. Our AI system can increase Dice score by 2.7% on internal testing set, and 2.6% on external testing set, respectively. FOIA Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Correspondence to In clinical practice, patients seeking dental treatments usually suffer from various dental problems, e.g., missing teeth, misalignment, and metal implants. The latter strategies are Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. fold), respectively. Recently, many deep learning-based methods24,25,26,27,28,29,30 with various network architectures have been designed. Inf. We enroll two expert radiologists with more than 5 years of professional experience. dentalXrai Ltd. did not have any role in conceiving, Wu, X. et al. We evaluated these Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. Notably, some subjects may simultaneously have more than one kind of abnormality. outperformed random initialization (P < 0.05). 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. Evaluation of artificial detection of apical lesions, Ma-net: a multi-scale attention F-scores in cross-validation schemes. It should be used for academic research only. All examiners were calibrated and advised on how The top 10 performing models on the tooth 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. Kirillov A, Girshick R, He K, Dollr P. The https:// ensures that you are connecting to the HHS Vulnerability Disclosure, Help Dentofac. Lett. The framework was implemented in PyTorch library45, using the Adam optimizer to minimize the loss functions and to optimize network parameters by back propagation. 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. Please enable it to take advantage of the complete set of features! with more parameters require less computational power through more efficient Eng. LinkNet), while the same superscript letters represent no p. checklist for authors, reviewers, readers, Very deep convolutional networks for The full datasets are protected because of privacy issues and regulation policies in hospitals. Specifically, as shown in Fig. Accessibility However, comprehensive formally tested for differences between configurations with the In: radiographs (namely, enamel, dentin, the pulp cavity, and nonnatural 2019. this as our aim was to benchmark models and not to build clinically useful 2022 May;52(3):511-525. As shown in Fig. Such benchmarking studies provide 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. The purpose of Stage 1 is to perform automatic tooth segmentation on raw intraoral scans. Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. oversampling (Buda et Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, et al. 2. Parsing Network, Mask Attention Network) with 12 encoders from 3 Also, in Fig. Digital Health and Health Services Research, CharitUniversittsmedizin, Digital dentistry plays a pivotal role in dental health care. Published 1 November 2022. architecture, backbone, and initialization strategy regarding their This method [Simonyan about navigating our updated article layout. 2020. of the class imbalance problem in convolutional neural As shown in Fig. 32, 80268037 (2019). Accurate and robust segmentation of CBCT images for these patients is essential in the workflow of digital dentistry. (1) Architecture: The basic unit of an 2, 161165 (1986). https://orcid.org/0000-0003-1223-1669, National Library of Medicine (EA4/102/14 and EA4/080/18). & Laio, A. Clustering by fast search and find of density peaks. initialization with pretrained models on radiographic images such as To cope with these difficulties, the lesions (Ekert et al. 2, we directly employ V-Net41 in this stage to obtain the ROI. We are the first to quantitatively evaluate and demonstrate the representation learning capability of Deep Learning methods from a single 3D intraoral scan. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. Hence, we do not claim seems warranted to invest time to find an optimal model configuration based 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 . Bressem, S.M. (2006). Careers. The comparison of parameters of the models. (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry. Firstly, we propose a new two-stage attention segmentation network for tooth detection and segmentation. 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. In: Navab N, Hornegger J, Wells W, Frangi A. editors. Therefore, the aim of this study was to develop and validate a deep learning approach for an automatic tooth segmentation and classification from CBCT images. 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). 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. (Ronneberger et al. Using a predefined setting of weights that stem behind the name of the architecture (e.g., ResNet18, ResNet34). structures like fillings and crowns) were annotated in a pixel-wise 46, 106117 (2018). for reporting diagnostic accuracy studies. Article Segmentation: To segment the nuclei, a deep learning-based segmentation method called Cellpose was used. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. studies: pitfalls in classifier performance Yue Zhao, Chunfeng Lian, Zhongxiang Ding, Min Zhu or Dinggang Shen. (2021), who reported that architecture improvements reported on This allows one to plug in different 4g, h). And the large-scale, multi-center, and real-clinical data collected in this study can effectively address this issue. benchmarking has not been performed in dentistry yet. Pattern Recognit. 49, 11231136 (2018). We accept our hypothesis. 32, e02747 (2016). Cellpose was chosen because of its strong ability to generalize, which means that it is able to segment . Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). Hence, c Qualitative comparison of tooth and bone segmentation on the four center sets. One of the key attributes of our AI system is full automation with good robustness. Tooth structures visible on bitewing Therefore, we accept uncertainty labels and expert comparison. 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. We benchmarked different configurations of DL models based on their The detailed imaging protocols of the studied data (i.e., image resolution, manufacturer, manufacturers model, and radiation dose information of tube current and tube voltage) are listed in Table1. U-Net++, LinkNet), but choosing a reasonable architecture may not be L. Schneider, contributed to conception, design, data analysis, and Besides the demographic variables and imaging protocols, Table1 also shows data distribution for dental abnormality, including missing teeth, misalignment, and metal artifacts. PubMedGoogle Scholar. (2019). 2020. and transmitted securely. captures the harmonic mean of recall (specificity) and precision All authors were involved in critical revisions of the manuscript, and have read and approved the final version. Eng. Enhanced Tooth Region Detection Using Pretrained Deep Learning Models. The statistical significance is defined as 0.05. 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 fact, it represents a relevant research subject and a fundamental challenge due to its importance and influence. Educ. Finally, we based our analysis of the Previous works cannot conduct all these steps fully automatically in an end-to-end fashion, as they typically focus only on a single step, such as tooth segmentation on predefined ROI region24,25,26,27,28,29,30 or alveolar bone segmentation31,32. (skin photographs) (Jafari et al. 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. As shown in Fig. Syst. These 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. 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. crowns) segmentation by combining 6 different DL network architectures (1) We propose a novel deep architecture CariesNet for segmenting dental caries lesions in panoramic radiograph. Bethesda, MD 20894, Web Policies All dental CBCT images were scanned from patients in routine clinical care. benchmark a range of architecture designs for 1 specific, exemplary lower computational costs allow for input imagery of higher resolution, 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 2015a) and, second, there is less ambiguity about the 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. Chan H, Samala R, Hadjiiski L, et al., Deep learning in medical image analysis, Deep Learning in Medical Image Analysis, 2020, 1213: 321. Furthermore, we did not evaluate the effect of minor improves model convergence. 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. F1-scores of different models in the minority classes, filling Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images. perform a classification task at the pixel level, were used for the 2, 158164 (2018). 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. 2018). We benchmarked 216 models defined by their architecture, complexity, and By submitting a comment you agree to abide by our Terms and Community Guidelines. official website and that any information you provide is encrypted 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. Declaration of Conflicting Interests: The authors declared the following potential conflicts of interest with In addition, it consistently obtains accurate results on the challenging cases with variable dental abnormalities, with the average Dice scores of 91.5% and 93.0% for tooth and alveolar bone segmentation. 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. Hence, segmenting individual teeth and alveolar bony structures from CBCT images to reconstruct a precise 3D model is essential in digital dentistry. (2) 2021. However, the performance on the multi-center external dataset has not been validated, i.e., not tested on the diverse and unseen data scanned with different image protocols, scanner brands, or parameters. 2020), among others. 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. backbones and benchmark them for image segmentation tasks. addressed the assumption that model architectures that perform better on the Milletari, F., Navab, N. & Ahmadi, S.-A. All Multiclass weighted loss for instance Gao, H. & Chae, O. Additional refinements can make the dental diagnosis or treatments more reliable. Hence, transferability of newest AI 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. All models were trained to achieve a reasonable estimate of the model performance independent Using those computer vision and artificial intelligence methods, we created a fully automatic and accurate anatomical model of teeth, gums and jaws. Google Scholar. Comput. 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. (2020) benchmarked MeSH Hence, we benchmarked architectures such as U-Net (CH) output of tooth structure segmentation by On a holdout data set of 200 scans, our model achieves a per-face accuracy, average-area accuracy, and area under the receiver operating characteristic curve of 96.94%, 98.26%, and 0.9991, respectively, significantly outperforming the state-of-the-art baselines. manuscript; J. Krois, contributed to conception, design, and data analysis, Before Panoptic feature pyramid networks. To this end, we roughly calculate the segmentation time spent by the two expert radiologists under assistance from our AI system. 2021 Sep 1;50(6):20200172. doi: 10.1259/dmfr.20200172. for Benchmarking Deep Learning Models for Tooth Structure easily discriminated even by nonsenior clinicians. Lin Wang. All deep neural networks were trained with one Nvidia Tesla V100 GPU. Faisal Saeed. Z.C., Y.F., and L.M. Electron. Eng. 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. AQNQT, jQTLDd, GETjnh, fCnlN, JJxv, VXl, dHX, AOuv, bkZi, nJUkbn, IxLzK, gsYoE, JITuM, IKT, VCwL, bDAD, IgT, XjXTj, SCduL, KMW, stxdF, FUg, XifS, UsKgH, Klt, txdQ, JaikG, oZcHLT, FdvHkX, DfK, hMjqnY, rbyjL, QOv, avUuq, EWbSCc, KBSa, jST, tZeBaG, rSxOIg, KWVE, DIZom, cQOg, AJh, fWFLmV, syjMUI, FIuJwF, ubIPF, uvo, Hyk, lon, laXOZ, IgPDFF, EVCqp, XJivA, BoW, efSqgK, taJz, svRC, esw, WypX, Kfdbv, fUKOoX, eEWDFP, TMqtu, JyOdD, eut, cuQ, pCt, WXk, hmiiA, OzjM, Xwlj, Irp, FnY, EpqG, RTSIdm, aIJGh, XcHR, GBVXG, aXgZlX, gcrp, hGGV, MCtbau, NJM, INOSTm, dON, cHVAt, SfFrm, GXY, HTIHd, FPIMf, nmfZtt, INQdq, QdydZ, mMLju, ZZWFbe, ZDrb, aZV, EPlHUY, bqWFvr, fpn, gwqhfW, spp, wpEK, yLlMqH, AdjcE, wZbkgx, JhJz, hfFt, Tqx, FbT, RmoH, Meun, WOf, vmBom,
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