Numbering teeth in panoramic images: A novel method based on deep learning and heuristic algorithm

Zanjani, 2021, Mask-mcnet: Tooth instance segmentation in 3d point clouds of intra-oral scans, Neurocomputing, 453, 286, 10.1016/j.neucom.2020.06.145 Hallikainen, 1996, History of panoramic radiography, Acta Radiologica, 37, 441, 10.1177/02841851960373P207 Vandenberghe, 2018, The digital patient - imaging science in dentistry, Journal of Dentistry, 74, S21, 10.1016/j.jdent.2018.04.019 Chen, 2020, Artificial intelligence in dentistry: current applications and future perspectives, Quintessence Int, 51, 248 Shen, 2017, Deep learning in medical image analysis, Annual Review of Biomedical Engineering, 19, 221, 10.1146/annurev-bioeng-071516-044442 Yeshua, 2019, Automatic detection and classification of dental restorations in panoramic radiographs, Issues in Informing Science and Information Technology, 16, 221, 10.28945/4306 Amer, 2015, An efficient segmentation algorithm for panoramic dental images, Procedia Computer Science, 65, 718, 10.1016/j.procs.2015.09.016 Silva, 2018, Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspectives, Expert Systems with Applications, 107, 15, 10.1016/j.eswa.2018.04.001 A. Wirtz, S.G. Mirashi, S. Wesarg, Automatic teeth segmentation in panoramic x-ray images using a coupled shape model in combination with a neural network, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11073 LNCS (2018) 712–719, doi: 10.1007/978-3-030-00937-3_81. G. Jader, J. Fontineli, M. Ruiz, K. Abdalla, M. Pithon, L. Oliveira, Deep instance segmentation of teeth in panoramic x-ray images, 2019, pp. 400–407, https://doi.org/10.1109/SIBGRAPI.2018.00058. T.L. Koch, M. Perslev, C. Igel, S.S. Brandt, Accurate segmentation of dental panoramic radiographs with u-nets, in: Proceedings - International Symposium on Biomedical Imaging, Vol. 2019-April, 2019, pp. 15–19, doi: 10.1109/ISBI.2019.8759563. M.P. Muresan, A.R. Barbura, S. Nedevschi, Teeth detection and dental problem classification in panoramic x-ray images using deep learning and image processing techniques, Proceedings - 2020 IEEE 16th International Conference on Intelligent Computer Communication and Processing, ICCP 2020 (2020) 457–463https://doi.org/10.1109/ICCP51029.2020.9266244. J. Redmon, S. Divvala, R. Girshick, A. Farhadi, You only look once: Unified, real-time object detection, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788. Lee, 2020, Application of a fully deep convolutional neural network to the automation of tooth segmentation on panoramic radiographs, Oral Surgery, Oral Medicine, Oral Pathology and Oral, Radiology, 129, 635 Shelhamer, 2014, Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640, 10.1109/TPAMI.2016.2572683 A.B. Oktay, Tooth detection with convolutional neural networks, 2017 Medical Technologies National Conference, TIPTEKNO 2017 2017-January (2017) 1–4, doi: 10.1109/TIPTEKNO.2017.8238075. E. Shaheen, A. Leite, K.A. Alqahtani, A. Smolders, A.V. Gerven, H. Willems, R. Jacobs, A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. a validation study, Journal of Dentistry 115 (2021) 103865, doi: 10.1016/j.jdent.2021.103865. Chung, 2021, Individual tooth detection and identification from dental panoramic x-ray images via point-wise localization and distance regularization, Artificial Intelligence in Medicine, 111, 10.1016/j.artmed.2020.101996 Leite, 2021, Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs, Clinical oral investigations, 25, 2257, 10.1007/s00784-020-03544-6 Vinayahalingam, 2021, Automated chart filing on panoramic radiographs using deep learning, Journal of Dentistry, 115, 10.1016/j.jdent.2021.103864 Muramatsu, 2021, Tooth detection and classification on panoramic radiographs for automatic dental chart filing: improved classification by multi-sized input data, Oral Radiology, 37, 13, 10.1007/s11282-019-00418-w Tuzoff, 2019, Tooth detection and numbering in panoramic radiographs using convolutional neural networks, Dentomaxillofacial Radiology, 48, 20180051, 10.1259/dmfr.20180051 Chen, 2019, A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films, Scientific Reports, 9, 1 J. Kim, H.S. Lee, I.S. Song, K.H. Jung, Dentnet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs, Scientific Reports 2019 9:1 9 (2019) 1–9, doi: 10.1038/s41598-019-53758-2. C. Kim, D. Kim, H.G. Jeong, S.J. Yoon, S. Youm, Automatic tooth detection and numbering using a combination of a cnn and heuristic algorithm, Applied Sciences 2020, Vol. 10, Page 5624 10 (2020) 5624, doi: 10.3390/app10165624. Estai, 2021, Deep learning for automated detection and numbering of permanent teeth on panoramic images, 50 Bilgir, 2021, An artificial intelligence approach to automatic tooth detection and numbering in panoramic radiographs, BMC Medical Imaging, 21, 1, 10.1186/s12880-021-00656-7 C. Gorurgoz, K. Orhan, I.S. Bayrakdar, O. Celik, E. Bilgir, A. Odabas, A.F. Aslan, R. Jagtap, Performance of a convolutional neural network algorithm for tooth detection and numbering on periapical radiographs, https://doi.org/10.1259/dmfr.20210246 50 (2021) 20210246, https://doi.org/10.1259/dmfr.20210246. Kilic, 2021, Artificial intelligence system for automatic deciduous tooth detection and numbering in panoramic radiographs, Dentomaxillofacial Radiology, 50, 20200172, 10.1259/dmfr.20200172 Lin, 2021, Tooth numbering and condition recognition on dental panoramic radiograph images using cnns, IEEE Access, 9, 166008, 10.1109/ACCESS.2021.3136026 Prados-Privado, 2021, A convolutional neural network for automatic tooth numbering in panoramic images, BioMed Research International, 2021, 1, 10.1155/2021/3625386 Tekin, 2022, An enhanced tooth segmentation and numbering according to fdi notation in bitewing radiographs, Computers in Biology and Medicine Kaya, 2022, Proposing a cnn method for primary and permanent tooth detection and enumeration on pediatric dental radiographs, Journal of Clinical Pediatric Dentistry, 46, 293, 10.22514/1053-4625-46.4.6 B. Silva, L. Pinheiro, L. Oliveira, M. Pithon, A study on tooth segmentation and numbering using end-to-end deep neural networks, Proceedings - 2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2020 (2020) 164–171https://doi.org/10.1109/SIBGRAPI51738.2020.00030. F.P. Mahdi, K. Motoki, S. Kobashi, Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs, Scientific Reports 2020 10:1 10 (2020) 1–12, doi: 10.1038/s41598-020-75887-9. Chandrashekar, 2022, Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs, Computers in Biology and Medicine, 148, 10.1016/j.compbiomed.2022.105829 Ren, 2017, Faster r-cnn: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137, 10.1109/TPAMI.2016.2577031 Akkaya, 2006, Comparing the accuracy of panoramic and intraoral radiography in the diagnosis of proximal caries, Dentomaxillofacial Radiology, 35, 170, 10.1259/dmfr/26750940 He, 2020, Mask r-cnn, IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386, 10.1109/TPAMI.2018.2844175 Karaoglu, 2021, Automatic dental segmentation module supported by artificial intelligence for dentistry students education, Artificial Intelligence Theory and Applications, 1, 180 Schwendicke, 2021, on behalf of the IADR e-oral health network, the ITU WHO focus group AI for Health, Artificial intelligence in dental research: Checklist for authors, reviewers, readers, Journal of dentistry, 107, 10.1016/j.jdent.2021.103610 K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.