{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:18:09Z","timestamp":1772824689383,"version":"3.50.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["CRF-C4026-21GF"],"award-info":[{"award-number":["CRF-C4026-21GF"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11517-023-02877-0","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T11:02:36Z","timestamp":1689678156000},"page":"2745-2755","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Domain adaptive Sim-to-Real segmentation of oropharyngeal organs"],"prefix":"10.1007","volume":"61","author":[{"given":"Guankun","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian-Ao","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiewen","family":"Lai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6488-1551","authenticated-orcid":false,"given":"Hongliang","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"issue":"1","key":"2877_CR1","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.mpaic.2013.11.007","volume":"15","author":"EB Thomas","year":"2014","unstructured":"Thomas EB, Moss S (2014) Tracheal intubation. Anaesth Intensiv Care Med 15(1):5\u20137","journal-title":"Tracheal intubation. Anaesth Intensiv Care Med"},{"issue":"1269\u20131277","key":"2877_CR2","first-page":"2","volume":"98","author":"RA Caplan","year":"2003","unstructured":"Caplan RA, Benumof JL, Berry FA, Blitt CD, Bode RH, Cheney FW, Connis RT, Guidry OF, Nickinovich DG, Ovassapian A (2003) Practice guidelines for management of the difficult airway. Anesthesiology 98(1269\u20131277):2","journal-title":"Anesthesiology"},{"issue":"4","key":"2877_CR3","doi-asserted-by":"publisher","first-page":"3794","DOI":"10.1109\/TASE.2021.3136185","volume":"19","author":"B Lu","year":"2021","unstructured":"Lu B, Li B, Chen W, Jin Y, Zhao Z, Dou Q, Heng PA, Liu Y (2021) Toward image-guided automated suture grasping under complex environments: a learning-enabled and optimization-based holistic framework. IEEE Transac Automation Sci Eng 19(4):3794\u20133808","journal-title":"IEEE Transac Automation Sci Eng"},{"issue":"2","key":"2877_CR4","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TMECH.2021.3078466","volume":"27","author":"J Lai","year":"2021","unstructured":"Lai J, Lu B, Chu HK (2021) Variable-stiffness control of a dual-segment soft robot using depth vision. IEEE ASME Trans Mechatron 27(2):1034\u20131045","journal-title":"IEEE ASME Trans Mechatron"},{"issue":"6","key":"2877_CR5","doi-asserted-by":"publisher","first-page":"5124","DOI":"10.1109\/TMECH.2022.3166522","volume":"27","author":"B Lu","year":"2022","unstructured":"Lu B, Li B, Dou Q, Liu Y (2022) A unified monocular camera-based and pattern-free hand-to-eye calibration algorithm for surgical robots with RCM constraints. IEEE\/ASME Trans Mechatron 27(6):5124\u20135135","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"2877_CR6","doi-asserted-by":"crossref","unstructured":"Yu BX, Liu Y, Zhang X, Zhong Sh, Chan KC (2022) Mmnet: a modelbased multimodal network for human action recognition in rgb-d videos. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3177813"},{"issue":"1","key":"2877_CR7","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S Asgari Taghanaki","year":"2021","unstructured":"Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54(1):137\u2013178","journal-title":"Artif Intell Rev"},{"issue":"3","key":"2877_CR8","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1109\/TMI.2018.2800298","volume":"37","author":"AF Frangi","year":"2018","unstructured":"Frangi AF, Tsaftaris SA, Prince JL (2018) Simulation and synthesis in medical imaging. IEEE Trans Med Image 37(3):673\u2013679","journal-title":"IEEE Trans Med Image"},{"issue":"6","key":"2877_CR9","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1007\/s10278-022-00656-1","volume":"35","author":"M Rehman","year":"2022","unstructured":"Rehman M, Arsenault L, Javan R (2022) Organs in color: utilizing free software and emerging multi jet fusion technology to color and surface label 3D-printed anatomical models. J Digit Imaging 35(6):1611\u20131622","journal-title":"J Digit Imaging"},{"key":"2877_CR10","doi-asserted-by":"crossref","unstructured":"Duriez C (2013) Control of elastic soft robots based on real-time finite element method. In: Proc IEEE Int Conf Robot Autom (ICRA), 3982-3987","DOI":"10.1109\/ICRA.2013.6631138"},{"key":"2877_CR11","doi-asserted-by":"crossref","unstructured":"Zhao W, Queralta JP, Westerlund T (2020) Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In: Proc IEEE Symp Ser Comput Intell (SSCI), 737-744","DOI":"10.1109\/SSCI47803.2020.9308468"},{"issue":"3","key":"2877_CR12","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.jormas.2017.03.002","volume":"118","author":"L Ganry","year":"2017","unstructured":"Ganry L, Hersant B, Quilichini J, Leyder P, Meningaud J (2017) Use of the 3D surgical modelling technique with open-source software for mandibular fibula free flap reconstruction and its surgical guides. J Stomatol Oral Maxillofac Surg 118(3):197\u2013202","journal-title":"J Stomatol Oral Maxillofac Surg"},{"key":"2877_CR13","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.ijom.2019.03.776","volume":"48","author":"R Pierri","year":"2019","unstructured":"Pierri R, Nogueira L, Balan I, Iwaki L et al (2019) Bimaxillary orthognatic surgery planned with the software blender, through the addon ortogonblender. Int J Oral Maxillofac Surg 48:254","journal-title":"Int J Oral Maxillofac Surg"},{"key":"2877_CR14","unstructured":"Chen X, Hu J, Jin C, Li L, Wang L (2021) Understanding domain randomization for sim-to-real transfer. arXiv:2110.03239"},{"key":"2877_CR15","doi-asserted-by":"crossref","unstructured":"Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P (2017) Domain randomization for transferring deep neural networks from simulation to the real world. In: IEEE\/RSJ Int. Conf. Intell. Robot. Syst. (IROS), pp. 23-30 . IEEE","DOI":"10.1109\/IROS.2017.8202133"},{"key":"2877_CR16","doi-asserted-by":"crossref","unstructured":"Yang Y, Soatto S (2020) Fda: Fourier domain adaptation for semantic segmentation. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4085-4095","DOI":"10.1109\/CVPR42600.2020.00414"},{"issue":"10","key":"2877_CR17","doi-asserted-by":"publisher","first-page":"2980","DOI":"10.1109\/TIP.2011.2134107","volume":"20","author":"B Geng","year":"2011","unstructured":"Geng B, Tao D, Xu C (2011) DAML: domain adaptation metric learning. IEEE Trans Image Process 20(10):2980\u20132989","journal-title":"IEEE Trans Image Process"},{"key":"2877_CR18","unstructured":"Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: Proc. Int. Conf. Mach. Learn. (ICML), pp. 97-105. PMLR"},{"key":"2877_CR19","unstructured":"Zellinger W, Grubinger T, Lughofer E, Natschl\u00e4ger T, SamingerPlatz S (2017) Central moment discrepancy (cmd) for domain-invariant representation learning. arXiv:1702.08811"},{"key":"2877_CR20","doi-asserted-by":"crossref","unstructured":"Zou Y, Yu Z, Kumar B, Wang J (2018) Unsupervised domain adaptation for semantic segmentation via class-balanced self-training. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 289-305","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"2877_CR21","doi-asserted-by":"crossref","unstructured":"Wu Z, Han X, Lin YL, Uzunbas MG, Goldstein T, Lim SN, Davis LS (2018) Dcan: dual channel-wise alignment networks for unsupervised scene adaptation. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 518-534","DOI":"10.1007\/978-3-030-01228-1_32"},{"key":"2877_CR22","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan S, Balaji Y, Jain A, Lim SN, Chellappa R (2018) Learning from synthetic data: addressing domain shift for semantic segmentation. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3752-3761","DOI":"10.1109\/CVPR.2018.00395"},{"key":"2877_CR23","doi-asserted-by":"crossref","unstructured":"Vu TH, Jain H, Bucher M, Cord M, P\u00e9rez P (2019) Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 2517-2526","DOI":"10.1109\/CVPR.2019.00262"},{"key":"2877_CR24","unstructured":"Hoffman J, Tzeng E, Park T, Zhu JY, Isola P, Saenko K, Efros A, Darrell T (2018) Cycada: cycle-consistent adversarial domain adaptation. In: Proc. Int. Conf. Mach. Learn. (ICML), pp. 1989-1998 . Pmlr"},{"key":"2877_CR25","doi-asserted-by":"crossref","unstructured":"Richter SR, Vineet V, Roth S, Koltun V (2016) Playing for data: ground truth from computer games. In: Proc. Eur. Conf. Comput. Vis. (ECCV), pp. 102-118. Springer","DOI":"10.1007\/978-3-319-46475-6_7"},{"key":"2877_CR26","doi-asserted-by":"crossref","unstructured":"Ros G, Sellart L, Materzynska J, Vazquez D, Lopez AM (2016) Thesynthia dataset: a large collection of synthetic images for semantic segmentation of urban scenes. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3234-3243","DOI":"10.1109\/CVPR.2016.352"},{"key":"2877_CR27","doi-asserted-by":"crossref","unstructured":"Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 3213-3223","DOI":"10.1109\/CVPR.2016.350"},{"key":"2877_CR28","doi-asserted-by":"crossref","unstructured":"Li Y, Yuan L, Vasconcelos N (2019) Bidirectional learning for domain adaptation of semantic segmentation. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 6936-6945","DOI":"10.1109\/CVPR.2019.00710"},{"key":"2877_CR29","unstructured":"Zhu XJ (2005) Semi-supervised learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences"},{"key":"2877_CR30","unstructured":"Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv:1511.06390"},{"key":"2877_CR31","doi-asserted-by":"crossref","unstructured":"An J, Huang S, Song Y, Dou D, Liu W, Luo J (2021) Artflow: unbiased image style transfer via reversible neural flows. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 862-871","DOI":"10.1109\/CVPR46437.2021.00092"},{"key":"2877_CR32","doi-asserted-by":"crossref","unstructured":"Lai J, Ren TA, Yue W, Su S, Chan JYK, Ren H (2023) Sim-to-real transfer of soft robotic navigation strategies that learns from the virtual eye-in-hand vision. Under Review","DOI":"10.1109\/TII.2023.3291699"},{"issue":"2","key":"2877_CR33","doi-asserted-by":"publisher","first-page":"4813","DOI":"10.1109\/LRA.2022.3152318","volume":"7","author":"J Lai","year":"2022","unstructured":"Lai J, Lu B, Zhao Q, Chu HK (2022) Constrained motion planning of a cable-driven soft robot with compressible curvature modeling. IEEE Robot Autom Lett 7(2):4813\u20134820","journal-title":"IEEE Robot Autom Lett"},{"key":"2877_CR34","unstructured":"Allan M, Shvets A, Kurmann T, Zhang Z, Duggal R, Su YH, Rieke N, Laina I, Kalavakonda N, Bodenstedt S, et al. (2019) 2017 robotic instrument segmentation challenge. arXiv:1902.06426"},{"key":"2877_CR35","unstructured":"Allan M, Kondo S, Bodenstedt S, Leger S, Kadkhodamohammadi R, Luengo I, Fuentes F, Flouty E, Mohammed A, Pedersen M, et al.: 2018 robotic scene segmentation challenge. arXiv:2001.11190"},{"key":"2877_CR36","unstructured":"University of Dundee, School of Medicine (2022): Pharynx and floor of mouth. https:\/\/skfb.ly\/6QXqr. Accessed: 2022-08-01"},{"key":"2877_CR37","doi-asserted-by":"crossref","unstructured":"Ghiasi G, Lee H, Kudlur M, Dumoulin V, Shlens J (2017) Exploringthe structure of a real-time, arbitrary neural artistic stylization network. arXiv:1705.06830","DOI":"10.5244\/C.31.114"},{"key":"2877_CR38","unstructured":"Li Y, Fang C, Yang J, Wang Z, Lu X, Yang MH (2017) Universal style transfer via feature transforms. Adv Neural Info Process Syst 30"},{"key":"2877_CR39","doi-asserted-by":"crossref","unstructured":"Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proc. IEEE Int. Conf. Compt. Vis. (ICCV), pp. 1501-1510","DOI":"10.1109\/ICCV.2017.167"},{"key":"2877_CR40","doi-asserted-by":"crossref","unstructured":"Liao J, Yao Y, Yuan L, Hua G, Kang SB (2017) Visual attribute transfer through deep image analogy. arXiv:1705.01088","DOI":"10.1145\/3072959.3073683"},{"key":"2877_CR41","unstructured":"Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. Adv Neural Info Process Syst 31"},{"key":"2877_CR42","unstructured":"Dinh L, Krueger D, Bengio Y (2014) Nice: non-linear independent components estimation. arXiv:1410.8516"},{"issue":"4","key":"2877_CR43","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 (2017) 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":"2877_CR44","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 770-778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2877_CR45","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02877-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02877-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02877-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T05:14:48Z","timestamp":1695791688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02877-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":45,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2877"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02877-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"5 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 June 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 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":"Ethical approval was not sought for the present study because this article does not contain any studies with human or animal subjects.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}