Accuracy in Teeth Structures Segmentation on MRI and CT Images:

A. Ramyaalakshmi Vels Institute of Science, Technology, and Advanced Studies, India S. Poonguzhali Vels Institute of Science, Technology, and Advanced Studies, India https://orcid.org/0000-0002-9118-9018 Accuracy in Teeth Structures Segmentation on MRI and CT Images
Electronic illustrative advances have advanced extensively in the previous ten years. Different picture-based applications have really organized man-made insight, which is a basic accomplishment. These tasks can help clinical experts in diagnosing, choosing, and organizing prescriptions. These undertakings are entirely important since they are reliable, fast, and prepared for performing tasks normally with the capability of arranged prepared experts. These models may be a significant resource for orthodontists with limited understanding. Most of these models, regardless, have explicit obstacles, for instance, the unassuming number of datasets used for planning and testing these models and the immovable nature of the data as a result of how it was accumulated from a single machine or clinical facility.
chapter 13 2023 10 18 218 235 10.4018/979-8-3693-0876-9.ch013 20231021111126 https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-0876-9.ch013 https://www.igi-global.com/viewtitle.aspx?TitleId=332836 10.1007/s10278-017-9983-4 10.1111/j.1365-2842.2010.02065.x 10.15537/smj.2017.12.20631 10.1016/j.ajodo.2020.08.014 10.1155/2020/5739312 10.1201/9781003429609-9 10.1371/journal.pone.0269198 10.1109/ICB.2012.6199831 10.1002/cnm.2747 10.1111/ocr.12542 10.1016/j.compbiomed.2022.105829 10.1109/ACCESS.2020.2991799 10.1097/SCS.0000000000005650 10.1111/ocr.12513 10.1002/14651858.ED000142 10.7861/futurehosp.6-2-94 Davenport, T., Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Futur Healthc J [Internet]. pmc/articles/PMC6616181/ 10.3390/ijerph19031728 10.1038/s41598-019-44839-3 10.1111/ocr.12502 10.1259/dmfr.20170220 10.1109/JBHI.2017.2709406 10.1016/j.ajodo.2021.11.011 Tooth Segmentation From Cone-Beam CT using graph cut. L. T.Hiew Proceedings of the Second APSIPA Annual Summit and Conference HiewL. T.OngS. H.FoongK. W. C. (2010). Tooth Segmentation From Cone-Beam CT using graph cut.Proceedings of the Second APSIPA Annual Summit and Conference, Biopolis, Singapore. 10.5624/isd.2019.49.1.1 10.1259/dmfr 10.1038/s41598-020-62321-3 Joiner, I. A. (2021). Artificial Intelligence. Emerg Libr Technol. Elsevier. https://linkinghub.elsevier.com/retrieve/pii/B97800 10.1016/j.ajodo.2015.07.030 10.3390/diagnostics11061004 10.1201/9781003429609-1 10.1201/9781003429609 10.1201/9781003356189 10.4018/978-1-6684-8851-5 10.1111/ocr.12514 10.1038/s41598-019-53758-2 10.1038/s41598-021-94362-7 10.5152/TurkJOrthod.2020.20059 10.1007/s00056-019-00203-8 10.1016/j.ajodo.2006.08.024 10.3390/jpm12030387 10.1016/j.oooo.2019.11.007 10.1007/s00784-020-03544-6 10.1177/1465312519840029 10.1038/s41598-018-38439-w 10.1016/j.ejrad.2009.03.042 10.4041/kjod.2022.52.2.112 10.1259/dmfr.20140313 Naumovich. S., Naumovich, S., & Goncharenko, V. (2015). Three dimensional reconstruction of teeth and jaws based on segmentation of CT images using watershed transformation. Dentomaxil lofacial Radiol, 44, 20140313. 10.1111/ocr.12521 10.1097/SCS.0000000000004901 10.3390/ijerph18126201 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., & Mulrow, C. D. (2022). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ [Internet].https://www.bmj.com/content/372/bmj.n71 10.2319/022019-127.1 10.1016/j.jdent.2022.104238 10.3390/jcm10163591 10.1016/j.oooo.2013.01.025 10.1016/j.oooo.2016.09.005 10.1016/j.eswa.2018.04.001 10.1155/2022/1880113 10.3390/bioengineering7020055 10.1088/1361-6560/aaf441 10.1038/sdata.2016.18 10.1007/978-3-030-00937-3_81 Wirtz, A., Mirashi, S. G., & Wesarg, S. (2018). Automatic teeth segmentation in panoramic X-Ray images using a coupled shape model in combination with a neural network. Paper presented at: Medical Image Computing and Computer Assisted Intervention – MICCAI. Springer. https://link.springer.com/chapter/10.1007/978-3-030-00937-3_81 10.1016/j.ajodo.2021.09.012 10.1177/0022034520901715 10.1016/j.media.2020.101904 10.1016/j.knosys.2020.106338 Zhao, Y., Li, P., Gao, C., Liu, Y., Chen, Q., Yang, F., & Meng, D. (2020). TSASNet: Tooth segmentation on dental panoramic X-ray images by Two-Stage Attention Segmentation Network. Knowl.-Based Syst., 206, 106338. https://www.sciencedirect.com/science/article/pii/S0950705120304950 10.3390/diagnostics11122200 10.1016/j.compbiomed.2022.106296