Part of Springer Nature. 2015. a. benchmarking has not been performed in dentistry yet. Accordingly, we also compute corresponding p values to validate whether the improvements are statistically significant. on the model performance. fold), respectively. F-scores in cross-validation schemes. 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. dental bitewing radiographs. Biol. We evaluated . An artifcial ntelligence approach to automatic tooth detection and numbering in panoramic radiographs. The experimental observations in Fig. input image. Carousel with three slides shown at a time. Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. 2020). initialization strategy. 2019. 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. Digital dentistry plays a pivotal role in dental health care. 2. detection of periodontal bone loss, Detection and Further information on research design is available in theNature Research Reporting Summary linked to this article. 20% of images, respectively. batch size of 32. The accurate detection and localization of tooth tissue on panoramic radiographs is the first step to identify pathology, and also plays a key role in an automatic diagnosis system. different architectures, encoder backbones, and IEEE Eng. Recent advancements in deep learning-based segmentation and object detection algorithms have enabled the provision of predictable and practical identification to assist in the evaluation of a patient's mineralized oral health, enabling dentists to construct a more successful treatment plan. Gong X, Chen S, Zhang B, et al., Style consistent image generation for nuclei instance segmentation, Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2021, 39944003. Thus, it is valuable to leverage the intra-oral scans to improve the tooth crown shapes reconstructed from CBCT images. Careers. Wu TH, Lian C, Lee S, Pastewait M, Piers C, Liu J, Wang F, Wang L, Chiu CY, Wang W, Jackson C, Chao WL, Shen D, Ko CC. (2022)Cite this article. 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. initialization or initialization based on pretrained weights from the Conf. These networks were selected, as Commons Attribution-NonCommercial 4.0 License (, sj-docx-1-jdr-10.1177_00220345221100169 Supplemental material All dental CBCT images were scanned from patients in routine clinical care. The red, dark green, light green, Furthermore, limited computational resources imply restrictions that 12 of 16 architectures benefited from an initialization with ImageNet 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. 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. 4g, h). Corpus ID: 245929386; A Deep Learning-based Method for Tooth Segmentation on Panoramic Dental X-ray Images @inproceedings{Kim2021ADL, title={A Deep Learning-based Method for Tooth Segmentation on Panoramic Dental X-ray Images}, author={Jin Kim and Su Yang and Min-Hyuk Choi and Sang-Jeong Lee and Bo-Soung Jeoun and Geonsoon Kim and Won-Jin Yi}, year={2021} } 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. Zhao, A., Balakrishnan, G., Durand, F., Guttag, J. V. & Dalca, A. V. Data augmentation using learned transformations for one-shot medical image segmentation. Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. performance between both initialization strategies. 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. architectures and encoder backbones and were each trained with 3 ISSN 2041-1723 (online). 2015b), U-Net++ (Zhou et al. Intelligence in Dental Research (Schwendicke et al. We proposed a novel tooth segmentation model combining deep-learning-based object detection methods and level set approaches. model configurations for a specific dental task: tooth structure (enamel, Google Scholar. The .gov means its official. predictions on class crowns (20%). One key element in those guidelines is a hypothesis-driven selection of the Our findings are consistent with those from Ke et al. which may be relevant for many dental applications. 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. they all allow to employ the same established backbones of varying In the present study, we aim to expand the studies of Bressem et al. Ekert T, Krois J, Meinhold L, Elhennawy K, Emara R, Golla T, Schwendicke F. the model performance. Comparing different 32, e02747 (2016). Gao, H. & Chae, O. J Syst Sci Complex (2022). This figure is Bressem, S.M. (positive predictive value [PPV]). combination with a ResNet50 backbone was 5 times smaller but reached an Given a predefined ROI, most of these learning-based methods can segment teeth automatically. Median, interquartile range, and 95% confidence interval are connections between them). Complexity: Most model architectures are available in of 16 convolutional architectures on 5 classification tasks. Individual tooth segmentation from CT images using level set method with shape and intensity prior. 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. most suitable to solve the underlying task. Panoptic feature pyramid networks. 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). available, with developers usually choosing one or a few of them for Milletari, F., Navab, N. & Ahmadi, S.-A. Several model development aspects were benchmarked. relationship between model depth and model performance. In: IEEE/CVF Conference on Computer Vision and Pattern Khalid, A. M. International designation system for teeth and areas of the oral cavity. trained on ImageNet yields a boost in performance (Ke et al. were not considered in the present study as they were very rare. networks. 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. We are the first to quantitatively evaluate and demonstrate the representation learning capability of Deep Learning methods from a single 3D intraoral scan. Multiclass weighted loss for instance oversampling (Buda et ImageNet data set (Deng neural transfer network for the detection of periodontal bone C of conditions k was computed (2006). 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. overfitting on ImageNet data sets. for Benchmarking Deep Learning Models for Tooth Structure comprehensive comparisons of existing study findings (Schwendicke et al. strategies (ImageNet, CheXpert, random initialization) were applied, Among all available options, CBCT imaging is a sole modality to provide comprehensive 3D volumetric information of complete teeth and alveolar bones. Hence, segmenting individual teeth and alveolar bony structures from CBCT images to reconstruct a precise 3D model is essential in digital dentistry. 1a, the acquired images present large style variations across different centers in terms of imaging protocols, scanner brands, and/or parameters. 2.2 Deep learning methods for image segmentation In dentistry, many methods have been proposed for com- Evaluation of artificial Please enable it to take advantage of the complete set of features! We benchmarked different configurations of DL models based on their They showed that complex and deep models do 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. Deep learning for automated detection and numbering of permanent teeth on panoramic images. Besides the demographic variables and imaging protocols, Table1 also shows data distribution for dental abnormality, including missing teeth, misalignment, and metal artifacts. the Appendix. Eng. (e.g., VGG13, VGG16, VGG19). Hence, we benchmarked architectures such as U-Net Digital Health and Health Services Research, CharitUniversittsmedizin, Esteva, A. et al. Parsing Network, Mask Attention Network) with 12 encoders from 3 These Before official website and that any information you provide is encrypted Hirschberg, J. To verify the clinical applicability of our AI system in more detail, we randomly selected 100 CBCT scans from the external set, and compared the segmentation results produced by our AI system and expert radiologists. wrote the code. architectures (U-Net, U-Net++, FPN, LinkNet, PSPNet, MAnet) with 2020. 3D Tooth Segmentation and Labeling Using Deep Convolutional Neural Networks. 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. U-Net++, LinkNet), but choosing a reasonable architecture may not be Moreover, the volume of tooth rapidly decreases after 50 years old due to tooth wear or broken, especially for molar teeth. 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. 43, 24062417 (2010). The number of pairwise comparisons To sum up, the main contributions of this work are threefold. or CheXpert, is consistently superior even when there is a difference in 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. Panoramic radiographs are an integral part of effective dental treatment planning, supporting dentists in identifying impacted teeth, infections, malignancies, and other dental issues. specialized layers extend the basic model architectures, which in such a resulting overall into 216 trained models, which were trained up to Image Anal. This is extremely important for an application developing for different institutions and clinical centers in real-world clinical practice. Specifically, instead of fully manual segmentation, the expert radiologists first apply our trained AI system to produce initial segmentation. Our results showed that there are The During 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. Model performances were primarily quantified by the F1-score, which It is based on deep learning neural networks and advanced mathematical algorithms from graph theory. This analysis is based on a segmentation task for tooth structures on Estai M, Tennant M, Gebauer D, Brostek A, Vignarajan J, Mehdizadeh M, Saha S. Dentomaxillofac Radiol. Figure 3 shows the F1-scores of 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. Bethesda, MD 20894, Web Policies We found statistically representations for efficient semantic 2021 Jul 12;23(7):e26151. family. In: different model architectures. Int. Chexpert: a large chest radiograph dataset with Chung, M. et al. fashion (as masks) by 1 dental expert. Xu X, Liu C, and Zheng Y, 3D tooth segmentation and labeling using deep convolutional neural networks, IEEE Transactions on Visualization and Computer Graphics, 2018, 25(7): 23362348. To obtain Several findings require a more detailed parameter efficiency of ImageNet models for chest X-ray 2020), or pathology (histological specimens) (Kather et al. Wang T, Qiao M, Zhang M, et al., Data-driven prognostic method based on self-supervised learning approaches for fault detection, Journal of Intelligent Manufacturing, 2020, 31(7): 16111619. Deep learning for the radiographic Before Images with implants, bridges, or root Yang, Y. et al. Berlin, Germany, 2ITU/WHO Focus Group on AI for bitewing radiographs. In this sense, we first apply an encoder-decoder network to automatically segment the foreground tooth for dental area localization. All deep neural networks were trained with one Nvidia Tesla V100 GPU. An official website of the United States government. this as our aim was to benchmark models and not to build clinically useful comparison instead of proposing a high-precision model. in comparison to the ground truth. Examples of segmented bitewing radiographs. Most of these patients need dental treatments, such as orthodontics, dental implants, and restoration. Illustration of the study design. School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China, Zhiming Cui,Yu Fang,Lanzhuju Mei,Jiameng Liu,Caiwen Jiang,Yuhang Sun,Lei Ma,Jiawei Huang&Dinggang Shen, Department of Computer Science, The University of Hong Kong, Hong Kong, 999077, China, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China, Shanghai Ninth Peoples Hospital, Shanghai Jiao Tong University, Shanghai, 200011, China, School of Public Health, Hangzhou Medical College, Hangzhou, 310013, China, Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, Chongqing, 401147, China, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China, School of Mathematics and Statistics, Xian Jiaotong University, Xian, 710049, China, Department of Radiology, Hangzhou First Peoples Hospital, Zhejiang University, Hangzhou, 310006, China, You can also search for this author in In International Workshop on Machine Learning in Medical Imaging, 242249 (Springer, 2012). Clipboard, Search History, and several other advanced features are temporarily unavailable. Artificial Neural Networks and Machine 2022 Springer Nature Switzerland AG. different depths (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, Tooth structures visible on bitewing In a second iteration, those 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 configurations on an identical data set. backbone family based on sample sizes n. depths of model layers (ResNet50 [He et al. Due to the retrospective nature of this study, the informed consent was waived by the relevant IRB. Second, it is hard to handle complicated cases commonly existing in clinical practice, e.g., CBCT images with dramatic-variations in structures scanned from patients with dental problems (e.g., missing teeth, misalignment, and metal artifacts). segmentation of cluttered cells, 2016. radiographs (namely, enamel, dentin, the pulp cavity, and nonnatural 2. Inf. We 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. Computed tomography data collection of the complete human mandible and valid clinical ground truth models, Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography, Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method, Clinically applicable artificial intelligence system for dental diagnosis with CBCT, Accuracy of digital model generated from CT data with metal artifact reduction algorithm, The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images, Convolutional neural network for automatic maxillary sinus segmentation on cone-beam computed tomographic images, Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans, Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography, https://pan.baidu.com/s/1LdyUA2QZvmU6ncXKl_bDTw, https://pan.baidu.com/s/194DfSPbgi2vTIVsRa6fbmA, http://creativecommons.org/licenses/by/4.0/, Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review, Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. 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. task. IEEE Access 8, 9729697309 (2020). architecture, backbone, and initialization strategy regarding their radiograph, while fillings and crowns were only available in 80% and Different superscript letters indicate As a qualitative evaluation, we show the representative segmentation produced by our AI system on both internal and external testing sets in Fig. This technique is referred to as transfer and JavaScript. performance on a tooth structure segmentation task of dental bitewing & Shen, D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Br. Bethesda, MD 20894, Web Policies 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. 2018). All requests will be promptly reviewed within 15 working days. Pattern Anal. under a 5-fold cross-validation scheme, where the combination of 2021. The largest network in the present study was c Qualitative comparison of tooth and bone segmentation on the four center sets. basically digits that correspond to the strength of the connection. Ke A, Ellsworth W, Banerjee O, Ng AY, Rajpurkar P. Schwendicke F, Golla T, Dreher M, Krois J. 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. 1Department of Oral Diagnostics, task) may provide guidance in the model development process and may and transmitted securely. Fan, Q., Yang, J., Hua, G., Chen, B. The accuracy across different teeth is consistently high, although the performance on the 3rd molars (i.e., the wisdom teeth) is slightly lower than other teeth. Provided by the Springer Nature SharedIt content-sharing initiative, Over 10 million scientific documents at your fingertips, Not logged in IEEE J. Biomed. on more complex models (e.g., from the ResNet family). 102:557-571. for medical image segmentation. 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. Dermatologist-level classification of skin cancer with deep neural networks. 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. Epub 2021 Oct 26. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. MWTNet is a semantic-based method for tooth instance segmentation by identifying boundaries between different teeth. V-net: Fully convolutional neural networks for volumetric medical image segmentation. Epub 2021 Mar 4. First, as reported, there is a significant tooth size discrepancy across people from different regions39,40. 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 network (i.e., the number of layers included and the number of neurons and Preventive and Pediatric Dentistry, Zahnmedizinische Kliniken der nonparametric Wilcoxon rank-sum test. canal, Linknet: exploiting encoder Peer reviewer reports are available. run, the data were randomly split into training, validation, and test potentially be more suitable for medical segmentation tasks of, for Nie, D. et al. Neural Inf. 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. increasing demands for computational resources, training time, or the need Hahn S, Perry M, Morris CS, Wshah S, Bertges DJ. regarding image resolution or batch size; both may negatively affect using a U-shaped deep convolutional network. We accept extensive hyperparameter search. 2021), the presence of caries lesions (Lee et al. Reporting of this Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. The overview network architecture is shown in Fig. It may be the case that model architectures Pytorch: an imperative style, high-performance deep learning library. Similarly, Ke et al. domain-specific tasks. We aimed to Additional refinements can make the dental diagnosis or treatments more reliable. 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 . inform dental researchers about suitable model configurations for their Hence, we do not claim For example, the predicted tooth roots may have a little over- or under-segmentation. 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 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. Hiew, L., Ong, S., Foong, K. W. & Weng, C. Tooth segmentation from cone-beam ct using graph cut. This will lead to a more accurate AI system for digital dentistry. 32, 80268037 (2019). samples in training, validation, and test set was varied for each fold there is evidence that segmentation models perform well on this task (Ronneberger et al. (3) Dent. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248255 (IEEE, 2009). provided statistical analysis and interpretation of the data. Results are based on a sample size International Publishing. Niehues, contributed to acquisition, critically revised the 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 These results demonstrate its potential as a powerful system to boost clinical workflows of digital dentistry. architectures were ranked according to their median F1-score and To account for lesions on bitewings (Cantu et al. Orthop. & Razionale, A. V. CT segmentation of dental shapes by anatomy-driven reformation imaging and b-spline modelling. convolutional neural network algorithm, A logical calculus of the ideas This assumption was not found to be valid based on the comparison Imagenet: a large-scale hierarchical Internet Explorer). MATH 2019. computed tomography scans. B.Z., B.Y., Y.L., Y.Z., Z.D., and M.Z. 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. official website and that any information you provide is encrypted Center-sensitive and boundary-aware tooth instance segmentation and classification from cone-beam CT. These masks represent the Using a predefined setting of weights that stem via equation (1). in-house custom-built annotation tool described in Ekert et al. Arsiwala-Scheppach, contributed to analysis, critically revised the Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method. Son J, Shin JY, Kim HD, Jung KH, Park KH, Park SJ. 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. Initialization: The connections between neurons and 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. 2020), among others. The framework was implemented in PyTorch library45, using the Adam optimizer to minimize the loss functions and to optimize network parameters by back propagation. Deep learning (DL) has been widely employed for image analytics in dermatology between complexity and model performance (F1-score). Med. Nat Commun 13, 2096 (2022). setting are referred to as backbone. Evain, T., Ripoche, X., Atif, J. Figure 1 shows the caries detection structure using U-net and Faster R-CNN in IOC images. Poplin, R. et al. Bergeest J and Rohr K, Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals, Medical Image Analysis, 2012, 16(7): 14361444. These low-level descriptors/features are sensitive to complicated appearances of dental CBCT images (e.g., limited intensity contrast between teeth and surrounding tissues), thus requiring tedious human interventions for initialization or post-correction. Segmentation of the tooth surface improves the overall caries detection performance by darkening areas not classified as tooth surfaces in each image. 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. 2016), ophthalmology (retina imagery) (Son et al. Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. model configurations. 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). 2019) and the will also be available for a limited time. Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study. Learn more Images with metal artifacts (a, b), missing teeth (c, d) and misalignment problems (e, f), and without dental abnormality (g, h). 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. containing millions of labeled images, also generally perform better on 2020. Bishara, S. E., Jakobsen, J. R., Abdallah, E. M. & Garcia, A. F. Comparisons of mesiodistal and bnccolingnal crown dimensions of the permanent teeth in three populations from egypt, mexico, and the united states. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 63686377 (2019). This site needs JavaScript to work properly. Our third objective, aimed to give insights whether initializing with ImageNet Disclaimer, National Library of Medicine With the volume and density changing curves as shown in Fig. In the training stage, we respectively adopt binary cross-entropy loss to supervise the tooth segmentation, and another L2 loss to supervise the 3D offset, tooth boundary, and apice prediction. Specifically, Dice is used to measure the spatial overlap between the segmentation result \(R\) and the ground-truth result G, defined as Dice=\(\frac{2\left|R\cap G\right|}{\left|R\right|+\left|G\right|}\). our hypothesis. Int. Keyhaninejad, S., Zoroofi, R., Setarehdan, S. & Shirani, G. Automated segmentation of teeth in multi-slice CT images. Barone, S., Paoli, A. However, the evaluation of panoramic radiographs depends on the clinical experience and knowledge of dentist, while the interpretation of panoramic radiographs might lead misdiagnosis. Deep learning can In the present study, 72 models were built from a combination of varying differs fundamentally from medical features of radiographs. Considering the field-of-view in 3D CBCT image usually captures the entire maxillofacial structures, the dental area is relatively small. From Supplementary Table3, we can have two important observations. 2021). & Laio, A. Clustering by fast search and find of density peaks. A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images. The segmentation accuracy is comprehensively evaluated in terms of three commonly used metrics, including Dice score, sensitivity, and average surface distance (ASD) error. 2021). government site. Deep residual learning for image 120, 103720 (2020). Each annotator independently assessed each image using an 8600 Rockville Pike 2017]). Unet++: a nested U-net architecture & Wipf, D. Revisiting deep intrinsic image decompositions. different radiographic extension on bitewings using deep 270279. Esteva A, Kuprel B, Novoa R, et al., Dermatologist-level classification of skin cancer with deep neural networks, Nature, 2017, 542(7639): 115118. Holm-Pedersen, P., Vigild, M., Nitschke, I. are represented by the white dot, the black box, and the black Ronneberger O, Fischer P, Brox T. 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. 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. learning (Tan et al. bone loss on panoramic radiographs (Kim et al. Model architectures such as 2021 Dec;115:103865. doi: 10.1016/j.jdent.2021.103865. 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). Some machinelearning-based methods . 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). Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. Lett. Before It should be used for academic research only. establishment of the ground truth for this task, with tooth structures being overview of segmentation outputs generated by different model architectures behind the name of the architecture (e.g., ResNet18, ResNet34). The 3D information of teeth and surrounding alveolar bones is essential and indispensable in digital dentistry, especially for orthodontic diagnosis and treatment planning. developing a standard evaluation process and benchmarking framework for Guerrero-Pen FA, Marrero Fernandez PD, Ing Ren T, Yui M, Rothenberg E, Cunha A. available in color online. Imaging furcation defects with low-dose cone beam computed tomography. Comput. that VGG backbones provided solid baseline models across different model Deeper and more complex models did not necessarily perform better than the BenjaminiHochberg method (Benjamini and Hochberg Mao M, Gao P, Zhang R, et al., Dual-stream network for visual recognition, Proceeings of Advances in Neural Information Processing Systems, 2021, 3446. 22, 196204 (2018). Bookshelf (skin photographs) (Jafari et al. In this work, we collected large-scale CBCT imaging data from multiple hospitals in China, including the Stomatological Hospital of Chongqing Medical University (CQ-hospital), the First Peoples Hospital of Hangzhou (HZ-hospital), the Ninth Peoples Hospital of Shanghai Jiao Tong University (SH-hospital), and 12 dental clinics. and, more so, dentistry, benchmarking initiatives are scarce, owing to Furthermore, radiographs with bridges, implants, and root canal fillings Proc Mach Learn Res. Proffit, W. R., Fields Jr, H. W. & Sarver, D. M. Contemporary Orthodontics (Elsevier Health Sciences, 2006). tasks. The comparison of weights for a classification task of chest radiographs. All authors gave final approval and agree to be accountable for Less complex model architectures may be In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 11971200 (IEEE, 2017). et al. measurement. HHS Vulnerability Disclosure, Help immanent in nervous activity. 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. 14197, Germany. 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. MAnet combined with a ResNet152 backbone, which reached an F1-score of 0.85 Nature Communications (Nat Commun) Also, the 3rd molars usually have significant shape variations, especially on the root area. MeSH . 49, 11231136 (2018). ones in this study. With the predicted tooth centroid points and skeletons, a fast clustering method42 is first implemented to distinguish each tooth based on the spatial centroid position, and simultaneously recognize tooth numbers. than 20,000 classes, while radiographic images contain grayscale The potential reasons are two-fold. structure segmentation task were built with backbones from the ResNet and 1995). Brain Mapp. In a recent benchmarking study, Bressem et al. Soc., 2021, 2021: 35653568. Fourth, we additionally found predictions on the minority class of filling On each model design, 3 initialization rate: a practical and powerful approach to multiple 8600 Rockville Pike and assessed model performances on underrepresented classes (in our This paper was recommended for publication by Editor QI Hongsheng. LearningICANN 2018. Hence, we did not In the second step of single tooth segmentation, the three-channel inputs to the multi-task tooth segmentation network are the patches cropped from the tooth centroid map, the skeleton map, and the tooth ROI images, respectively. conducting, and reporting of DL studies in dentistry (Schwendicke et al. Unable to load your collection due to an error, Unable to load your delegates due to an error. An official website of the United States government. Dis. Note that these two expert radiologists are not the people for ground-truth label annotation. statistically significant. instance, dental radiographs. J. Pak. performance, independent of the origin of transferred knowledge. Segmentation of Deep Learning Software market: By Type: Software,Hardware,Service. 16 different model architectures for classification tasks on 2 openly 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). and D.S. The proposal-based methods are sensitive to the localization results due to the lack of local cues, while the proposal-free methods have poor clustering outputs because of the affinity measured by the low-level characteristics, especially in situations of tightly . Declaration of Conflicting Interests: The authors declared the following potential conflicts of interest with The .gov means its official. Details on training are described in the Shaheen E, Leite A, Alqahtani KA, Smolders A, Van Gerven A, Willems H, Jacobs R. J Dent. Focus Group AI for Health. 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. Faisal Saeed. backbones from 3 different families (ResNet, VGG, DenseNet) of multiple comparisons, we adjusted the P values using In this study, SWin-Unet, the transformer-based Ushaped encoder-decoder architecture with skip-connections, is introduced to perform panoramic radiograph segmentation. initialized with 3 different strategies (random, ImageNet, CheXpert), learning. This analysis is based on a segmentation task for tooth structures on dental bitewing radiographs. These results further highlight the advantage of conducting segmentation in the 3D space (i.e., by our AI system) rather than 2D slice-by-slice operations (i.e., by expert radiologists). 2022 Nov 9;22(1):480. doi: 10.1186/s12903-022-02514-6. The present study will inform (2021) 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. apical lesions on cone beam computed tomography scans (Orhan et al. Initialization with Deep embedding convolutional neural network for synthesizing ct image from t1-weighted mr image. for reporting diagnostic accuracy studies. 96, 416422 (1989). 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). A clinical performance test of 500 patients with malocclusion and/or abnormal teeth shows that 96.9% of the segmentations are satisfactory for clinical applications, 2.9% automatically trigger alarms for human improvement, and only 0.2% of them need rework. To this end, we roughly calculate the segmentation time spent by the two expert radiologists under assistance from our AI system. Models developed and found superior on https://orcid.org/0000-0002-6010-8940, F. Schwendicke Deep learning for the radiographic It should be highlighted that respect to the research, authorship, and/or publication of this All examiners were calibrated and advised on how using deep learning. As represented in Figure 1, models were built by combining different model different encoder families (ResNet, VGG, DenseNet) of varying depth The https:// ensures that you are connecting to the Would you like email updates of new search results? Med. Google Scholar. Image Anal. Dentomaxillofac Radiol. relationship between model performances and model complexity exclusively on existing model architectures. the ResNet family. This stage includes three steps: pre-processing, inference, and post-processing. 2020), periodontal CharitUniversittsmedizin Berlin, Berlin, Germany, Supplemental material, sj-docx-1-jdr-10.1177_00220345221100169 for n. This figure is available in color Based on the This may be relevant for the implementation of 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. First, we aimed to evaluate whether there are superior model architectures for Notably, however, the number of parameters increased in ImageNet may not always be translated to performances on medical imaging 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. Hum. investigated the model performances emanating from model complexity. The https:// ensures that you are connecting to the 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. Cybern. We deliberately decided to use this application since first, Article Table3 shows that the two expert radiologists take 147 and 169min (on average) to annotate one CBCT scan, respectively. Another important contribution of this study is that we have conducted a series of experiments and clinical applicability tests on a large-scale dataset collected from multi-center clinics, demonstrating that deep learning has great potential in digital dental dentistry. J. Numer. Adv. Kirillov A, Girshick R, He K, Dollr P. We discovered a performance advantage Thank you for visiting nature.com. Models known to perform better than others Bookshelf & Sun, L. Medical image enhancement algorithm based on wavelet transform. 47, 3144 (2018). Geonet++: iterative geometric neural network with edge-aware refinement for joint depth and surface normal estimation. Abstracts of Presentations at the Association of Clinical Scientists 143. He, K., Gkioxari, G., Dollr, P. & Girshick, R. Mask r-cnn. We used a 5-fold cross-validation scheme The corresponding results are summarized in Table3. 2021. 200 epochs with the Adam optimizer (learning rate = 0.0001) and a F1-scores stratified by initialization strategy, architecture, and segmentation in clinical images using deep Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. 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