{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T19:44:03Z","timestamp":1774295043142,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T00:00:00Z","timestamp":1674518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004704","name":"National Research Council of Thailand","doi-asserted-by":"crossref","award":["484\/2563"],"award-info":[{"award-number":["484\/2563"]}],"id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11517-022-02761-3","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T08:47:36Z","timestamp":1674550056000},"page":"1193-1207","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Encoder-decoder network with RMP for tongue segmentation"],"prefix":"10.1007","volume":"61","author":[{"given":"Worapan","family":"Kusakunniran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6309-5022","authenticated-orcid":false,"given":"Punyanuch","family":"Borwarnginn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarattha","family":"Karnjanapreechakorn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kittikhun","family":"Thongkanchorn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panrasee","family":"Ritthipravat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pimchanok","family":"Tuakta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paitoon","family":"Benjapornlert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,24]]},"reference":[{"issue":"12","key":"2761_CR1","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 (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2761_CR2","unstructured":"BioHit (2014) Tongeimagedataset. https:\/\/github.com\/BioHit\/TongeImageDataset"},{"issue":"22","key":"2761_CR3","doi-asserted-by":"publisher","first-page":"e5849","DOI":"10.1002\/cpe.5849","volume":"32","author":"Y Cai","year":"2020","unstructured":"Cai Y, Wang T, Liu W, Luo Z (2020) A robust interclass and intraclass loss function for deep learning based tongue segmentation. Concurr Comput Pract Experience 32(22):e5849","journal-title":"Concurr Comput Pract Experience"},{"key":"2761_CR4","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected CRFS. arXiv:14127062"},{"issue":"4","key":"2761_CR5","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017a) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans Pattern Anal Mach Intell 40(4):834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2761_CR6","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H (2017b) Rethinking atrous convolution for semantic image segmentation. arXiv:170605587"},{"key":"2761_CR7","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"2761_CR8","doi-asserted-by":"crossref","unstructured":"Cui Z, Zuo W, Zhang H, Zhang D (2013) Automated tongue segmentation based on 2D Gabor filters and fast marching. In: International conference on intelligent science and big data engineering. Springer, pp 328\u2013335","DOI":"10.1007\/978-3-642-42057-3_42"},{"key":"2761_CR9","doi-asserted-by":"crossref","unstructured":"Fachrurrozi M et al (2017) Tongue segmentation using active contour model. In: IOP conference series: Materials science and engineering, vol 190. IOP Publishing, p 012041","DOI":"10.1088\/1757-899X\/190\/1\/012041"},{"issue":"10","key":"2761_CR10","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: context encoder network for 2D medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"key":"2761_CR11","doi-asserted-by":"crossref","unstructured":"Guo J, Yang Y, Wu Q, Su J, Ma F (2016) Adaptive active contour model based automatic tongue image segmentation. In: 2016 9th international congress on image and signal processing, Biomedical engineering and informatics (CISP-BMEI). IEEE, pp 1386\u20131390","DOI":"10.1109\/CISP-BMEI.2016.7852933"},{"key":"2761_CR12","doi-asserted-by":"crossref","unstructured":"Kriman S, Beliaev S, Ginsburg B, Huang J, Kuchaiev O, Lavrukhin V, Leary R, Li J, Zhang Y (2020) Quartznet: deep automatic speech recognition with 1D time-channel separable convolutions. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 6124\u20136128","DOI":"10.1109\/ICASSP40776.2020.9053889"},{"key":"2761_CR13","doi-asserted-by":"crossref","unstructured":"Le DN, Parvathy VS, Gupta D, Khanna A, Rodrigues JJ, Shankar K (2021) IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification. Int J Mach Learn Cybern: 1\u201314","DOI":"10.22541\/au.162912530.05037483\/v1"},{"key":"2761_CR14","doi-asserted-by":"crossref","unstructured":"Li J, Xu B, Ban X, Tai P, Ma B (2017a) A tongue image segmentation method based on enhanced HSV convolutional neural network. In: International conference on cooperative design, visualization and engineering. Springer, pp 252\u2013260","DOI":"10.1007\/978-3-319-66805-5_32"},{"issue":"14","key":"2761_CR15","doi-asserted-by":"publisher","first-page":"e5714","DOI":"10.1002\/cpe.5714","volume":"32","author":"L Li","year":"2020","unstructured":"Li L, Luo Z, Zhang M, Cai Y, Li C, Li S (2020) An iterative transfer learning framework for cross-domain tongue segmentation. Concurr Comput Pract Experience 32(14):e5714","journal-title":"Concurr Comput Pract Experience"},{"key":"2761_CR16","doi-asserted-by":"crossref","unstructured":"Li X, Yang T, Hu Y, Xu M, Zhang W, Li F (2017b) Automatic tongue image matting for remote medical diagnosis. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 561\u2013564","DOI":"10.1109\/BIBM.2017.8217710"},{"key":"2761_CR17","doi-asserted-by":"crossref","unstructured":"Lin B, Xie J, Li C, Qu Y (2018) Deeptongue: tongue segmentation via resnet. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1035\u20131039","DOI":"10.1109\/ICASSP.2018.8462650"},{"key":"2761_CR18","doi-asserted-by":"publisher","first-page":"41372","DOI":"10.1109\/ACCESS.2020.2976826","volume":"8","author":"W Liu","year":"2020","unstructured":"Liu W, Zhou C, Li Z, Hu Z (2020) Patch-driven tongue image segmentation using sparse representation. IEEE Access 8:41372\u201341383","journal-title":"IEEE Access"},{"key":"2761_CR19","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2761_CR20","doi-asserted-by":"crossref","unstructured":"Mehta S, Rastegari M, Caspi A, Shapiro L, Hajishirzi H (2018) Espnet: efficient spatial pyramid of dilated convolutions for semantic segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 552\u2013568","DOI":"10.1007\/978-3-030-01249-6_34"},{"key":"2761_CR21","doi-asserted-by":"publisher","first-page":"146308","DOI":"10.1109\/ACCESS.2020.3015108","volume":"8","author":"KB Park","year":"2020","unstructured":"Park KB, Choi SH, Lee JY (2020) M-gan: retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access 8:146308\u2013146322","journal-title":"IEEE Access"},{"key":"2761_CR22","unstructured":"Paszke A, Chaurasia A, Kim S, Culurciello E (2016) Enet: a deep neural network architecture for real-time semantic segmentation. arXiv:160602147"},{"key":"2761_CR23","unstructured":"Pinheiro PO, Collobert R, Doll\u00e1r P (2015) Learning to segment object candidates. arXiv:150606204"},{"key":"2761_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"2761_CR25","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1145\/1015706.1015720","volume":"23","author":"C Rother","year":"2004","unstructured":"Rother C, Kolmogorov V, Blake A (2004) \u201cgrabcut\u201d interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23(3):309\u2013314","journal-title":"ACM Trans Graph (TOG)"},{"key":"2761_CR26","doi-asserted-by":"crossref","unstructured":"Shen X, Tao X, Gao H, Zhou C, Jia J (2016) Deep automatic portrait matting. In: European conference on computer vision. Springer, pp 92\u2013107","DOI":"10.1007\/978-3-319-46448-0_6"},{"key":"2761_CR27","doi-asserted-by":"publisher","first-page":"106153","DOI":"10.1016\/j.dib.2020.106153","volume":"32","author":"D Shi","year":"2020","unstructured":"Shi D, Tang C, Blackley SV, Wang L, Yang J, He Y, Bennett SI, Xiong Y, Shi X, Zhou L et al (2020) An annotated dataset of tongue images supporting geriatric disease diagnosis. Data Brief 32:106153","journal-title":"Data Brief"},{"key":"2761_CR28","doi-asserted-by":"publisher","unstructured":"Tang C (2019) Replication data for: an annotated dataset of tongue images. https:\/\/doi.org\/10.7910\/DVN\/COJZMQ","DOI":"10.7910\/DVN\/COJZMQ"},{"issue":"9","key":"2761_CR29","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/JBHI.2020.2986376","volume":"24","author":"Q Xu","year":"2020","unstructured":"Xu Q, Zeng Y, Tang W, Peng W, Xia T, Li Z, Teng F, Li W, Guo J (2020) Multi-task joint learning model for segmenting and classifying tongue images using a deep neural network. IEEE J Biomed Health Inform 24(9):2481\u20132489","journal-title":"IEEE J Biomed Health Inform"},{"key":"2761_CR30","doi-asserted-by":"crossref","unstructured":"Xue Y, Li X, Wu P, Li J, Wang L, Tong W (2018) Automated tongue segmentation in chinese medicine based on deep learning. In: International conference on neural information processing. Springer, pp 542\u2013553","DOI":"10.1007\/978-3-030-04239-4_49"},{"issue":"4","key":"2761_CR31","doi-asserted-by":"publisher","first-page":"312","DOI":"10.3390\/app7040312","volume":"7","author":"R Yasrab","year":"2017","unstructured":"Yasrab R, Gu N, Zhang X (2017) An encoder-decoder based convolution neural network (CNN) for future advanced driver assistance system (ADAS). Appl Sci 7(4):312","journal-title":"Appl Sci"},{"key":"2761_CR32","unstructured":"Zeng X, Zhang Q, Chen J, Zhang G, Zhou A, Wang Y (2020) Boundary guidance hierarchical network for real-time tongue segmentation. arXiv:200306529"},{"key":"2761_CR33","doi-asserted-by":"publisher","first-page":"148779","DOI":"10.1109\/ACCESS.2019.2946681","volume":"7","author":"C Zhou","year":"2019","unstructured":"Zhou C, Fan H, Li Z (2019a) Tonguenet: accurate localization and segmentation for tongue images using deep neural networks. IEEE Access 7:148779\u2013148789","journal-title":"IEEE Access"},{"issue":"15","key":"2761_CR34","doi-asserted-by":"publisher","first-page":"3128","DOI":"10.3390\/app9153128","volume":"9","author":"J Zhou","year":"2019","unstructured":"Zhou J, Zhang Q, Zhang B, Chen X (2019b) Tonguenet: a precise and fast tongue segmentation system using u-net with a morphological processing layer. Appl Sci 9(15):3128","journal-title":"Appl Sci"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02761-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02761-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02761-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T01:12:56Z","timestamp":1681089176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02761-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,24]]},"references-count":34,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["2761"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02761-3","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,24]]},"assertion":[{"value":"6 February 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}}]}}