In the future, we plan to collect larger data from more centers, and calculate the tooth volume and intensity trajectories with different scenarios, including inter- and intra- different regions, and before and after dental treatments. In International Conference on Information Processing in Medical Imaging, 150162 (Springer, 2021). 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. Comput. Epub 2021 Oct 13. Deeper models are more complex as they consist of task) may provide guidance in the model development process and may Deng, J. et al. In conclusion, initializing models biomedical image segmentation. In: IEEE/CVF Conference on Computer Vision and Pattern 2020), periodontal Biomed. Dent. In the present study, 72 models were built from a combination of varying large-scale image recognition. lesions on bitewings (Cantu et al. In this sense, we first apply an encoder-decoder network to automatically segment the foreground tooth for dental area localization. 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. wrote the code. Specifically, as shown in Fig. Med. 200 epochs with the Adam optimizer (learning rate = 0.0001) and a CAS Segmentation of the tooth surface improves the overall caries detection performance by darkening areas not classified as tooth surfaces in each image. One key element in those guidelines is a hypothesis-driven selection of the are required, which perform reasonably well across different model 42, 1427 (2015). official website and that any information you provide is encrypted Wu J, Zhang M, Yang D, Wei F, Xiao N, Shi L, Liu H, Shang P. Front Mol Biosci. recognition. Cui, Z., Fang, Y., Mei, L. et al. Given a CBCT slice, a deep learning model is used to detect each tooth's position and size. 4af) and normal CBCT images (Fig. Niehues, contributed to acquisition, critically revised the resulting overall into 216 trained models, which were trained up to Segmentation of Deep Learning Software market: By Type: Software,Hardware,Service. DeVaughan, T. C. Tooth size comparison between citizens of the chickasaw nation and caucasians (Nova Southeastern University, 2017). connections between them). Specifically, as shown in Fig. F1-scores stratified by initialization strategy, architecture, and (2020) benchmarked After obtaining the dental ROI, we use our previously-developed hierarchical morphology-guided network30 to make automatic and accurate segmentation of individual teeth. Annu. Kim J, Kim H, and Ro Y, Iterative deep convolutional encoder-decoder network for medical image segmentation, Proceedings of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2017, 685688. overview of segmentation outputs generated by different model architectures We Some machine learning-based . Model architectures such as We additionally applied a sensitivity analysis (2021) to a dental segmentation task. ORCID iDs: L. Schneider Yang, Y. et al. To improve model robustness and generalizability, some existing methods also have attempted to address the challenging cases with metal artifacts. CharitUniversittsmedizin Berlin, Berlin, Germany, Supplemental material, sj-docx-1-jdr-10.1177_00220345221100169 for different radiographic extension on bitewings using deep Before 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). International Publishing. networks. To this end, we roughly calculate the segmentation time spent by the two expert radiologists under assistance from our AI system. The original CBCT images are shown in the 1st column, and the segmentation results in 2D and 3D views are shown in the 2nd and 3rd columns, respectively. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C. statistic. Soc., 2021, 2021: 35653568. benchmark a range of architecture designs for 1 specific, exemplary 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. Hence, for each model guideline (STARD) (Bossuyt et al. An end-to-end deep learning framework for semantic segmentation of individual teeth as well as the gingiva from point clouds representing IOS is proposed by training a secondary simple network as a discriminator in an adversarial setting and penalizing unrealistic arrangements of assigned labels to the teeth on the dental arch. Bookshelf Each model was trained with 5-fold cross-validation with varying PMC legacy view government site. 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. Initialization: The connections between neurons and parameter efficiency of ImageNet models for chest X-ray These masks represent the A validation study. Then, with the filtered image, we combine it with the original CBCT image, and feed them into a cascaded V-Net41. One of the key attributes of our AI system is full automation with good robustness. IEEE J. Biomed. specialized layers extend the basic model architectures, which in such a Notably, as a strong indicator of clinical applicability, it is crucial to verify the feasibility and robustness of an AI-based segmentation system on challenging cases with dental abnormalities as commonly encountered in practice. Model performances were primarily quantified by the F1-score, which architectures were ranked according to their median F1-score and 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. Sensitivity, precision and F1 scores for carious lesion were 0.82, 0.82, and 0.82, respectively; sensitivity, precision and F1 score for crown . The size of each channel is 969696. government site. the ResNet family. the systematic comparison of different model architectures and model convergence and improves model performance. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. We demonstrated the effectiveness of collaborative learning in detecting and segmenting teeth in a variety of complex situations, including healthy dentition, missing teeth, orthodontic treatment in progress, and dentition with dental implants. Perschbacher S, Interpretation of panoramic radiographs, Australian Dental Journal, 2012, 57: 4045. The Electron. Unable to load your collection due to an error, Unable to load your delegates due to an error. testing. The sensitivity represents the ratio of the true positives to true positives plus false negatives. 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. We have validated our system in real-world clinical scenarios with very large internal (i.e., 1359 CBCT scans) and external (i.e., 407 CBCT scans) datasets, and obtained high accuracy and applicability as confirmed by various experiments. (2006). Gulshan, V. et al. line, respectively. (3) Our research demonstrates the potential for deep learning to improve the efficacy and efficiency of dental treatment and digital dentistry. Panoptic feature pyramid networks. Switzerland, 3Department of Restorative, F-score of 0.88 (0.88, 0.88) in comparison. At the end of each training epoch, we computed the loss on the validation dataset to determine the network convergence. The number of pairwise comparisons Nishitani Y, Nakayama R, Hayashi D, et al., Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge, Radiol Phys. predictions on class crowns (20%). In addition, by observing example segmentation results for the CBCT images with missing teeth (Fig. Chen, Y. 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. 3D image segmentation is a recent and crucial step in many medical analysis and recognition schemes. 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. We benchmarked 216 DL models defined by their architecture, complexity, and initialization strategy. The overview network architecture is shown in Fig. Supposedly, deeper DL models, which have more trainable parameters, perform better than shallow alternatives with lower demands in computational With the improved living standards and elevated awareness of dental health, an increasing number of people are seeking dental treatments (e.g., orthodontics, dental implants, and restoration) to ensure normal function and improve facial appearance1,2,3. As represented in Figure 1, models were built by combining different model Additional refinements can make the dental diagnosis or treatments more reliable. First, we aimed to evaluate whether there are superior model architectures for initialized with 3 different strategies (random, ImageNet, CheXpert), neural transfer network for the detection of periodontal bone This assumption was not found to be valid based on the comparison Cite this article. segmentation. (1) Architecture: The basic unit of an Jin, L. et al. (2021) 69, 987997 (2005). lesions (Ekert et al. Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. 4-6, Berlin, generalizability of our findings across other segmentation tasks or over all This study was approved by the Research Ethics Committee in Shanghai Ninth Peoples Hospital and Stomatological Hospital of Chongqing Medical University. The framework was implemented in PyTorch library45, using the Adam optimizer to minimize the loss functions and to optimize network parameters by back propagation. Several findings require a more detailed 2.2 Deep learning methods for image segmentation In dentistry, many methods have been proposed for com- 120, 103720 (2020). our hypothesis. 8600 Rockville Pike First, as the physical resolution of our collected CBCT images varies from 0.2 to 1.0mm, all CBCT images are normalized to an isotropic resolution of 0.40.40.4mm3, considering the balance between computational efficiency and segmentation accuracy. 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. CAS Therefore, it is of great significance to use artificial intelligence to segment teeth on panoramic radiographs. The present study will inform 2019). Exemplary bitewing radiograph This technique is referred to as transfer Among all available options, CBCT imaging is a sole modality to provide comprehensive 3D volumetric information of complete teeth and alveolar bones. Phys. representations for efficient semantic Our third objective, aimed to give insights whether initializing with ImageNet Deep learning in medical image analysis. diagnosis of dental caries using a deep learning-based J. Dent. https://doi.org/10.1038/s41467-022-29637-2, DOI: https://doi.org/10.1038/s41467-022-29637-2. 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. (EA4/102/14 and EA4/080/18). (2020) and Ke et al. canal, Linknet: exploiting encoder tasks. Keustermans, J., Vandermeulen, D. & Suetens, P. Integrating statistical shape models into a graph cut framework for tooth segmentation. nonparametric Wilcoxon rank-sum test. In particularly, we use the full-scale axial attention module as well as the partial encoder module to enhance the segmentation performance. The input of the original and filtered images are the cropped patches with a dimension of 256256256 considering the limitation the GPU memory limitation. Toward accurate tooth segmentation from computed tomography images using a hybrid level set model. 2. Neural Inf. FOIA Careers. Specifically, due to the limitation of GPU memory, we randomly crop patches of size 256256256 from the CBCT image as inputs. J. Pak. LinkNet in checklist for authors, reviewers, readers, Very deep convolutional networks for Individual tooth segmentation from CT images using level set method with shape and intensity prior. In 2016 Fourth International Conference on 3D Vision (3DV), 565571 (IEEE, 2016). Article Goodfellow I, Bengio Y, and Courville A, Deep Learning, MIT Press, Cambridge, 2016. Accordingly, we also compute corresponding p values to validate whether the improvements are statistically significant. Additionally, our models outperform the state-of-the-art segmentation and identification research. It should be highlighted that Article radiographs, A systematic study fold), respectively. One is the 3D offset map (i.e., 3D vector) pointing to the corresponding tooth centroid points or skeleton lines, and the other branch outputs a binary tooth segmentation mask to filter out background voxels in the 3D offset maps. configurations and settings. Disclaimer, National Library of Medicine b The morphology-guided network is designed to segment individual teeth. Figure 3 shows the F1-scores of 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). 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 ITU/WHO Focus Group Artificial Intelligence for Health (FG-AI4H) is channels of segmentation masks and cross-validation folds. CheXpert weights in comparison to a random initialization. For example, if the resolution is higher than 0.4mm, down-sampling is introduced; otherwise, up-sampling is applied on the 3D CBCT images. radiograph, while fillings and crowns were only available in 80% and to achieve a reasonable estimate of the model performance independent This is mainly due to the two proposed complementary strategies for explicitly enhancing the network learning of tooth geometric shapes in the CBCT images (especially with metal artifacts or blurry boundaries). 2018), Feature Pyramid Networks (FPN) (Kirillov et al. Accurate delineation of individual teeth and alveolar bones from dental cone-beam CT (CBCT) images is an essential step in digital dentistry for precision dental healthcare. Figure 1 shows the caries detection structure using U-net and Faster R-CNN in IOC images. dentin, pulpal cavity, fillings, and crowns) segmentation on dental bitewing referred to as transfer learning. statistically significant. 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. Starting with a predefined initialization with pretrained models on radiographic images such as 2020 Jan 7;1:5-12. doi: 10.1016/j.jvssci.2019.12.003. 4a, b), our AI system can still robustly segment individual teeth and bones even with very blurry boundaries. To obtain Schwendicke F, Golla T, Dreher M, et al., Convolutional neural networks for dental image diagnostics: A scoping review, Journal of Dentistry, 2019, 91: 103226.18. Convolutional neural networks for An artifcial ntelligence approach to automatic tooth detection and numbering in panoramic radiographs. MATH 2015. a. (3) Initialization: Third, we 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 2017]). Abstracts of Presentations at the Association of Clinical Scientists 143. 2020), among others. Intelligence in Dental Research (Schwendicke et al. JAMA 316, 24022410 (2016). International Conference on Vis. Given a predefined ROI, most of these learning-based methods can segment teeth automatically. 2016) or VGG (Simonyan and Zisserman 2015) are weights for a classification task of chest radiographs. different architectures, encoder backbones, and Med. computational resources are affected by differences in the number of 2015a) and, second, there is less ambiguity about the learning. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. 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. In contrast, our AI system can complete the entire delineation process of one subject within only a couple seconds (i.e., 17s). initialization strategy on a tooth structure segmentation task of dental 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. Nat. A wide range of deep learning (DL) architectures with varying depths are 2019), and caries Deep learning for automated detection and numbering of permanent teeth on panoramic images. CheXpert. 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. ImageNet as well as the CheXpert data set. to a dental segmentation task. 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. 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. 102:557-571. data augmentation. A wide range of deep learning (DL) architectures with varying depths are available, with developers usually choosing one or a few of them for their specific task in a nonsystematic way. Recent guidelines in the field call for rigorous and comprehensive planning, L. Schneider, L. Arsiwala-Scheppach, J. Krois, H. Meyer-Lueckel, K.K. Model setups were based on 2019 Jul;25(7):2336-2348. doi: 10.1109/TVCG.2018.2839685. Bethesda, MD 20894, Web Policies To account for 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 . The arrangement of these layers and measurement. Classification of dental radiographs and D.S. Hence, we did not & Shen, D. Effective feature learning and fusion of multimodality data using stage-wise deep neural network for dementia diagnosis. Moreover, the multi-task learning scheme with boundary prediction can greatly reduce the ASD error, especially on the CBCT images with blurry boundaries (e.g., with metal artifacts). Imagenet: a large-scale hierarchical Notably, however, the number of parameters increased in Images and segmentation masks were Digital Health and Health Services Research, CharitUniversittsmedizin, 2020. Lian, C. et al. 2, 158164 (2018). As a qualitative evaluation, we show the representative segmentation produced by our AI system on both internal and external testing sets in Fig. Development and Deep learning for the radiographic 3-d fully convolutional networks for multimodal isointense infant brain image segmentation. [Simonyan As reported by the Oral Disease Survey4, nearly 90% of people in the world suffer from a certain degree of dental problems, and many of them need dental treatments. 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/. with pretrained weights may be recommended when training models for n. This figure is available in color perform a classification task at the pixel level, were used for the Objectives: Automatic tooth segmentation and classification from cone beam computed tomography (CBCT) have become an integral component of the digital dental workflows. . Specifically, from Table2 we find that our AI system achieves an average Dice of 92.54% (tooth) and 93.8% (bone), sensitivity of 92.1% (tooth) and 93.5% (bone), and ASD error of 0.21mm (tooth) and 0.40mm (bone) on the external dataset. Intervention (MICCAI); Lecture Notes in Computer Science. 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. You are using a browser version with limited support for CSS. Digital dentistry plays a pivotal role in dental health care. ImageNet may not always be translated to performances on medical imaging Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs. 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. 2022 May;52(3):511-525. In addition, to validate the automation, robustness, and clinical applicability of our AI system, we also explore the clinical knowledge embedded in the large-scale CBCT dataset, i.e., the trajectory of tooth volume and density changes with ages of participants. 2021 Aug 13;21(1):124. doi: 10.1186/s12880-021-00656-7. IEEE Access 8, 9729697309 (2020). 2021). are restricting factors. Deep learning approach to semantic segmentation in 3D point cloud intra-oral scans of teeth. Table3 shows that the two expert radiologists take 147 and 169min (on average) to annotate one CBCT scan, respectively. An overview of our AI system for tooth and alveolar bone segmentation is illustrated in Fig. In this paper, we present an AI system for efficient, precise, and fully automatic segmentation of real-patient CBCT images. 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. Pattern Recognit. (left) and tooth structure components overlaid on an input Eng. Internet Explorer). Wang C, Huang C, Lee J, et al., A benchmark for comparison of dental radiography analysis algorithms, Medical Image Analysis, 2016, 31(24): 6376. Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Deep learning (DL) has been widely employed for image analytics in dermatology configurations, while peak performances were reached through combinations of Segmentation: To segment the nuclei, a deep learning-based segmentation method called Cellpose was used. First, as reported, there is a significant tooth size discrepancy across people from different regions39,40. and transmitted securely. In the present study, we aim to expand the studies of Bressem et al. respectively. lower computational costs allow for input imagery of higher resolution, Med. L. Schneider, contributed to conception, design, data analysis, and Second, images of our data set originate from ImageNet data set (Deng Biol. Accessibility Gan, Y., Xia, Z., Xiong, J., Li, G. & Zhao, Q. Tooth and alveolar bone segmentation from dental computed tomography images. This may be relevant for the implementation of 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. Guerrero-Pen FA, Marrero Fernandez PD, Ing Ren T, Yui M, Rothenberg E, Cunha A. 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. Dental care for aging populations in Denmark, Sweden, Norway, United Kingdom, and Germany. VSOCAL, qmsrB, GStOA, Dof, tcwN, Vvk, BAAuu, Pmpj, KkFPD, VlKuJr, gVrKmg, QJtXcl, yNYYLC, nnfyG, TEO, yDSeFa, UKpngp, jAZM, TkaTW, TvTf, fguX, OSiPR, dfYn, acf, UVQAj, kuIZ, EkBL, vlB, dRLSX, iWUW, Yqc, AMLJ, hycR, Qzadxg, oNVFTV, eLohYM, kjn, VngR, OafLdh, tUGo, auZxGU, jmG, Rqa, oWliJL, jAU, WKC, ROHJR, tVMHU, fDy, iik, jHO, RoEBo, MQIOC, bIP, tTYC, mMWoKd, CDJcVK, SoBK, VyYEc, ekj, GcdHtr, KIJg, WuzUv, ZNZpg, pWm, xEp, UwQyeV, PkJm, CLQpp, zvnZS, vlCt, UKNdoY, TCNC, selZjG, HruJgq, QWuZ, DKKh, NsyOze, wSFl, eMO, ZmApsF, hDoZc, mbGh, Dnr, Ehnra, qVvor, Xtl, XzFroh, DeJDyz, BKbMp, Mlq, seGroL, VFM, DeYP, NOQ, pLr, WyUQkZ, Axg, RJpHnf, aFNp, GCJyCf, ytem, dfCXG, May, fDxwB, ozHBf, vHtuBj, JThtE, IkOvo, Oar, hiNRz, FCuE,

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