{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T13:21:08Z","timestamp":1794057668127,"version":"4.8.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001700","name":"Ministry of Education, Culture, Sports, Science and Technology of Japan","doi-asserted-by":"crossref","award":["20K18604"],"award-info":[{"award-number":["20K18604"]}],"id":[{"id":"10.13039\/501100001700","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.<\/jats:p>","DOI":"10.1038\/s41598-021-04354-w","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T11:03:21Z","timestamp":1641553401000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning"],"prefix":"10.1038","volume":"12","author":[{"given":"Shota","family":"Ito","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuichi","family":"Mine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuki","family":"Yoshimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saori","family":"Takeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akari","family":"Tanaka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Azusa","family":"Onishi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tzu-Yu","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Nakamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toshikazu","family":"Nagasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoya","family":"Kakimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Murayama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kotaro","family":"Tanimoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"key":"4354_CR1","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1177\/0333102416686302","volume":"37","author":"T List","year":"2017","unstructured":"List, T. & Jensen, R. H. Temporomandibular disorders: Old ideas and new concepts. Cephalalgia 37, 692\u2013704 (2017).","journal-title":"Cephalalgia"},{"key":"4354_CR2","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1056\/NEJMra0802472","volume":"359","author":"SJ Scrivani","year":"2008","unstructured":"Scrivani, S. J., Keith, D. A. & Kaban, L. B. Temporomandibular disorders. N. Engl. J. Med. 359, 2693\u20132705 (2008).","journal-title":"N. Engl. J. Med."},{"key":"4354_CR3","doi-asserted-by":"publisher","first-page":"T20","DOI":"10.1016\/j.jpain.2013.07.014","volume":"14","author":"GD Slade","year":"2013","unstructured":"Slade, G. D. et al. Signs and symptoms of first-onset TMD and sociodemographic predictors of its development: The OPPERA prospective cohort study. J. Pain 14, T20-32.e323 (2013).","journal-title":"J. Pain"},{"key":"4354_CR4","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1016\/j.ijom.2020.04.013","volume":"49","author":"H Stimmer","year":"2020","unstructured":"Stimmer, H. et al. Lesions of the lateral pterygoid muscle-an overestimated reason for temporomandibular dysfunction: A 3T magnetic resonance imaging study. Int. J. Oral Maxillofac. Surg. 49, 1611\u20131617 (2020).","journal-title":"Int. J. Oral Maxillofac. Surg."},{"key":"4354_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/s11282-021-00521-x","author":"M Johnson","year":"2021","unstructured":"Johnson, M., Sreela, L. S., Mathew, P. & Prasad, T. S. Actual applications of magnetic resonance imaging in dentomaxillofacial region. Oral Radiol. https:\/\/doi.org\/10.1007\/s11282-021-00521-x (2021).","journal-title":"Oral Radiol."},{"key":"4354_CR6","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/S1079-2104(05)80154-4","volume":"80","author":"TA Larheim","year":"1995","unstructured":"Larheim, T. A. Current trends in temporomandibular joint imaging. Oral Surg. Oral Med Oral Pathol. Oral Radiol. Endod. 80, 555\u2013576 (1995).","journal-title":"Oral Surg. Oral Med Oral Pathol. Oral Radiol. Endod."},{"key":"4354_CR7","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.ijom.2019.07.005","volume":"49","author":"J Lei","year":"2020","unstructured":"Lei, J., Yap, A. U., Li, Y., Liu, M. Q. & Fu, K. Y. Clinical protocol for managing acute disc displacement without reduction: A magnetic resonance imaging evaluation. Int. J. Oral. Maxillofac. Surg. 49, 361\u2013368 (2020).","journal-title":"Int. J. Oral. Maxillofac. Surg."},{"key":"4354_CR8","doi-asserted-by":"publisher","first-page":"8495","DOI":"10.1038\/s41598-019-44839-3","volume":"9","author":"J Krois","year":"2019","unstructured":"Krois, J. et al. Deep learning for the radiographic detection of periodontal bone loss. Sci. Rep. 9, 8495. https:\/\/doi.org\/10.1038\/s41598-019-44839-3 (2019).","journal-title":"Sci. Rep."},{"key":"4354_CR9","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.jpor.2019.08.006","volume":"64","author":"Y Mine","year":"2020","unstructured":"Mine, Y., Suzuki, S., Eguchi, T. & Murayama, T. Applying deep artificial neural network approach to maxillofacial prostheses coloration. J. Prosthodont. Res. 64, 296\u2013300 (2020).","journal-title":"J. Prosthodont. Res."},{"key":"4354_CR10","doi-asserted-by":"publisher","first-page":"20200171","DOI":"10.1259\/dmfr.20200171","volume":"50","author":"R Kuwana","year":"2021","unstructured":"Kuwana, R. et al. Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs. Dentomaxillofac. Radiol. 50, 20200171. https:\/\/doi.org\/10.1259\/dmfr.20200171 (2021).","journal-title":"Dentomaxillofac. Radiol."},{"key":"4354_CR11","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1038\/s41598-021-81449-4","volume":"11","author":"JH Yoo","year":"2021","unstructured":"Yoo, J. H. et al. Deep learning based prediction of extraction difficulty for mandibular third molars. Sci. Rep. 11, 1954. https:\/\/doi.org\/10.1038\/s41598-021-81449-4 (2021).","journal-title":"Sci. Rep."},{"key":"4354_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jds.2020.10.012","author":"S Takeda","year":"2020","unstructured":"Takeda, S. et al. Landmark annotation and mandibular lateral deviation analysis of posteroanterior cephalograms using a convolutional neural network. J. Dent. Sci. https:\/\/doi.org\/10.1016\/j.jds.2020.10.012 (2020).","journal-title":"J. Dent. Sci."},{"key":"4354_CR13","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1177\/0022034520915714","volume":"99","author":"F Schwendicke","year":"2020","unstructured":"Schwendicke, F., Samek, W. & Krois, J. Artificial intelligence in dentistry: Chances and challenges. J. Dent. Res. 99, 769\u2013774 (2020).","journal-title":"J. Dent. Res."},{"key":"4354_CR14","doi-asserted-by":"publisher","first-page":"e200078","DOI":"10.1148\/ryai.2021200078","volume":"3","author":"AD Desai","year":"2021","unstructured":"Desai, A. D. et al. The international workshop on osteoarthritis imaging knee MRI segmentation challenge: a multi-institute evaluation and analysis framework on a standardized dataset. Radiol. Artif. Intell. 3, e200078. https:\/\/doi.org\/10.1148\/ryai.2021200078 (2021).","journal-title":"Radiol. Artif. Intell."},{"key":"4354_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 9351, 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"4354_CR16","unstructured":"Badrinarayanan, V., Handa, A., & Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. Preprint at https:\/\/arxiv.org\/abs\/1505.07293 (2015)."},{"key":"4354_CR17","unstructured":"Python. https:\/\/www.python.org\/."},{"key":"4354_CR18","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014).","journal-title":"J. Mach. Learn. Res."},{"key":"4354_CR19","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A. & Cipolla, R. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 2481\u20132495 (2017).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4354_CR20","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1002\/jmri.26860","volume":"51","author":"L Zhang","year":"2020","unstructured":"Zhang, L. et al. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI. J. Magn. Reson. Imaging 51, 635\u2013643 (2020).","journal-title":"J. Magn. Reson. Imaging"},{"key":"4354_CR21","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.patcog.2019.03.004","volume":"91","author":"J Wei","year":"2019","unstructured":"Wei, J., Xia, Y. & Zhang, Y. M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation. Pattern Recognit. 91, 366\u2013378 (2019).","journal-title":"Pattern Recognit."},{"key":"4354_CR22","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1002\/jmri.26766","volume":"51","author":"E Gr\u00f8vik","year":"2020","unstructured":"Gr\u00f8vik, E. et al. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J. Magn. Reson. Imaging 51, 175\u2013182 (2020).","journal-title":"J. Magn. Reson. Imaging"},{"key":"4354_CR23","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1111\/joor.12016","volume":"40","author":"M Naeije","year":"2013","unstructured":"Naeije, M., Te Veldhuis, A. H., Te Veldhuis, E. C., Visscher, C. M. & Lobbezoo, F. Disc displacement within the human temporomandibular joint: A systematic review of a \u201cnoisy annoyance\u201d. J. Oral Rehabil. 40, 139\u2013158 (2013).","journal-title":"J. Oral Rehabil."},{"key":"4354_CR24","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1067\/moe.2000.106300","volume":"90","author":"B Nebbe","year":"2000","unstructured":"Nebbe, B. et al. Magnetic resonance imaging of the temporomandibular joint: Interobserver agreement in subjective classification of disk status. Oral Surg. Oral Med Oral Pathol. Oral Radiol. Endod. 90, 102\u2013107 (2000).","journal-title":"Oral Surg. Oral Med Oral Pathol. Oral Radiol. Endod."},{"key":"4354_CR25","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1259\/dmfr\/23427399","volume":"35","author":"SE Widmalm","year":"2006","unstructured":"Widmalm, S. E., Brooks, S. L., Sano, T., Upton, L. G. & McKay, D. C. Limitation of the diagnostic value of MR images for diagnosing temporomandibular joint disorders. Dentomaxillofac. Radiol. 35, 334\u2013338 (2006).","journal-title":"Dentomaxillofac. Radiol."},{"key":"4354_CR26","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1259\/dmfr\/16076453","volume":"39","author":"KW Butzke","year":"2010","unstructured":"Butzke, K. W., Batista Chaves, K. D., Dias da Silveira, H. E. & Dias da Silveira, H. L. Evaluation of the reproducibility in the interpretation of magnetic resonance images of the temporomandibular joint. Dentomaxillofac. Radiol. 39, 157\u2013161 (2010).","journal-title":"Dentomaxillofac. Radiol."},{"key":"4354_CR27","doi-asserted-by":"crossref","unstructured":"Zhu, Q., Du, B., Turkbey, B., Choyke, P. L. & Yan, P. Deeply-supervised CNN for prostate segmentation. In International Joint Conference on Neural Networks IEEE 178\u2013184 (2017).","DOI":"10.1109\/IJCNN.2017.7965852"},{"key":"4354_CR28","doi-asserted-by":"crossref","unstructured":"Lee, H. J., Kim, J. U., Lee, S., Kim, H. G., & Ro, Y. M. Structure Boundary Preserving Segmentation for Medical Image with Ambiguous Boundary. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 4817\u20134826 (2020).","DOI":"10.1109\/CVPR42600.2020.00487"},{"key":"4354_CR29","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N. & Rahman, M. S. MultiResUNet\u202f: Rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020).","journal-title":"Neural Netw."},{"key":"4354_CR30","doi-asserted-by":"publisher","first-page":"5711","DOI":"10.1038\/s41598-020-62586-8","volume":"10","author":"GH Kwak","year":"2020","unstructured":"Kwak, G. H. et al. Automatic mandibular canal detection using a deep convolutional neural network. Sci. Rep. 10, 5711. https:\/\/doi.org\/10.1038\/s41598-020-62586-8 (2020).","journal-title":"Sci. Rep."},{"key":"4354_CR31","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1002\/mrm.26841","volume":"79","author":"F Liu","year":"2018","unstructured":"Liu, F. et al. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn. Reson. Med. 79, 2379\u20132391 (2018).","journal-title":"Magn. Reson. Med."},{"key":"4354_CR32","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.compmedimag.2018.04.009","volume":"67","author":"P de Dumast","year":"2018","unstructured":"de Dumast, P. et al. A web-based system for neural network based classification in temporomandibular joint osteoarthritis. Comput. Med. Imaging Graph. 67, 45\u201354 (2018).","journal-title":"Comput. Med. Imaging Graph."},{"key":"4354_CR33","doi-asserted-by":"publisher","first-page":"1095021","DOI":"10.1117\/12.2506018","volume":"10950","author":"NT Ribera","year":"2019","unstructured":"Ribera, N. T. et al. Shape variation analyzer: A classifier for temporomandibular joint damaged by osteoarthritis. Proc. SPIE Int. Soc. Opt. Eng. 10950, 1095021. https:\/\/doi.org\/10.1117\/12.2506018 (2019).","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"4354_CR34","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1177\/0022034520936950","volume":"99","author":"KS Lee","year":"2020","unstructured":"Lee, K. S. et al. Automated detection of TMJ osteoarthritis based on artificial intelligence. J. Dent. Res. 99, 1363\u20131367 (2020).","journal-title":"J. Dent. Res."},{"key":"4354_CR35","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1177\/0022034519865187","volume":"98","author":"B Shoukri","year":"2019","unstructured":"Shoukri, B. et al. Minimally invasive approach for diagnosing TMJ Osteoarthritis. J. Dent. Res. 98, 1103\u20131111 (2019).","journal-title":"J. Dent. Res."},{"key":"4354_CR36","doi-asserted-by":"publisher","first-page":"8012","DOI":"10.1038\/s41598-020-64942-0","volume":"10","author":"J Bianchi","year":"2020","unstructured":"Bianchi, J. et al. Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning. Sci. Rep. 10, 8012. https:\/\/doi.org\/10.1038\/s41598-020-64942-0 (2020).","journal-title":"Sci. Rep."},{"key":"4354_CR37","doi-asserted-by":"publisher","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","volume":"104","author":"J Waring","year":"2020","unstructured":"Waring, J., Lindvall, C. & Umeton, R. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif. Intell. Med. 104, 101822. https:\/\/doi.org\/10.1016\/j.artmed.2020.101822 (2020).","journal-title":"Artif. Intell. Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-04354-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-04354-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-04354-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T20:08:09Z","timestamp":1669320489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-04354-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["4354"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-04354-w","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]},"assertion":[{"value":"12 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"221"}}