{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T17:45:54Z","timestamp":1788457554209,"version":"build-2803163510"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["N2319003"],"award-info":[{"award-number":["N2319003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Biomed Eng"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1007\/s10439-024-03510-3","type":"journal-article","created":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T14:48:10Z","timestamp":1714574890000},"page":"2101-2117","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI"],"prefix":"10.1007","volume":"52","author":[{"given":"Zi\u2019an","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7812-9750","authenticated-orcid":false,"given":"Yin","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,1]]},"reference":[{"key":"3510_CR1","unstructured":"Zhou, H.-Y., J. Guo, Y. Zhang, L. Yu, L. Wang, and Yu, Y. nnFormer: interleaved transformer for volumetric segmentation, 2021. arXiv preprint arXiv:2109.03201."},{"key":"3510_CR2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., N. He, J. Yang, Y. Li, D. Wei, Y. Huang, Y. Zhang, Z. He, and Y. Zheng. mmFormer: multimodal medical transformer for incomplete multimodal learning of brain tumor segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. Springer, 2022, pp. 107\u2013117.","DOI":"10.1007\/978-3-031-16443-9_11"},{"issue":"10","key":"3510_CR3","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.3390\/electronics12102174","volume":"12","author":"S Cai","year":"2023","unstructured":"Cai, S., C. Shen, and X. Wang. Energy-based MRI semantic augmented segmentation for unpaired CT images. Electronics. 12(10):2174, 2023.","journal-title":"Electronics"},{"key":"3510_CR4","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.586197","volume":"14","author":"R Su","year":"2020","unstructured":"Su, R., J. Liu, D. Zhang, C. Cheng, and M. Ye. Multimodal glioma image segmentation using dual encoder structure and channel spatial attention block. Front. Neurosci.14:586197, 2020.","journal-title":"Front. Neurosci."},{"key":"3510_CR5","doi-asserted-by":"crossref","unstructured":"Qu, L., S. Liu, M. Wang, and Z. Song. TransMEF: a transformer-based multi-exposure image fusion framework using self-supervised multi-task learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, pp. 2126\u20132134.","DOI":"10.1609\/aaai.v36i2.20109"},{"issue":"2","key":"3510_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., P. F. Jaeger, S. A. Kohl, J. Petersen, and K. H. Maier-Hein. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods. 18(2):203\u2013211, 2021.","journal-title":"Nat. Methods"},{"issue":"3","key":"3510_CR7","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3):209\u2013249, 2021.","journal-title":"CA Cancer J. Clin."},{"issue":"9","key":"3510_CR8","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.1002\/mp.14378","volume":"47","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Y. Lei, Y. Fu, T. Wang, J. Zhou, X. Jiang, M. McDonald, J. J. Beitler, W. J. Curran, T. Liu, et al. Head and neck multi-organ auto-segmentation on CT images aided by synthetic MRI. Med. Phys. 47(9):4294\u20134302, 2020.","journal-title":"Med. Phys."},{"issue":"11","key":"3510_CR9","doi-asserted-by":"publisher","first-page":"5389","DOI":"10.1007\/s00405-022-07455-y","volume":"279","author":"E Gunduz","year":"2022","unstructured":"Gunduz, E., O. F. Al\u00e7in, A. Kizilay, and I. O. Yildirim. Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors. Eur. Arch. Oto-Rhino-Laryngol. 279(11):5389\u20135399, 2022.","journal-title":"Eur. Arch. Oto-Rhino-Laryngol."},{"key":"3510_CR10","doi-asserted-by":"publisher","first-page":"40360","DOI":"10.1109\/ACCESS.2021.3064752","volume":"9","author":"H Zhang","year":"2021","unstructured":"Zhang, H., H. Lai, Y. Wang, X. Lv, Y. Hong, J. Peng, Z. Zhang, C. Chen, and C. Chen. Research on the classification of benign and malignant parotid tumors based on transfer learning and a convolutional neural network. IEEE Access. 9:40360\u201340371, 2021.","journal-title":"IEEE Access"},{"issue":"2","key":"3510_CR11","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neurad.2009.10.005","volume":"38","author":"JL Goyault","year":"2011","unstructured":"Goyault, J. L., S. Riehm, A. Neuville, A. Gentine, and F. Veillon. Interest of diffusion-weighted and gadolinium-enhanced dynamic MR sequences for the diagnosis of parotid gland tumors. J. Neuroradiol. 38(2):77\u201389, 2011.","journal-title":"J. Neuroradiol."},{"key":"3510_CR12","doi-asserted-by":"crossref","unstructured":"Madani, G., and T. Beale. Tumors of the salivary glands. In: Seminars in Ultrasound, CT and MRI, vol. 27. Amsterdam: Elsevier, 2006, pp. 452\u2013464.","DOI":"10.1053\/j.sult.2006.09.004"},{"issue":"8","key":"3510_CR13","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Y. Gao, and F. Liu. TransMED: transformers advance multi-modal medical image classification. Diagnostics. 11(8):1384, 2021.","journal-title":"Diagnostics"},{"issue":"7","key":"3510_CR14","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1017\/S0022215119001130","volume":"133","author":"T Nakano","year":"2019","unstructured":"Nakano, T., R. Yasumatsu, R. Kogo, K. Hashimoto, K. Asai, S. Ohga, H. Yamamoto, T. Nakashima, and T. Nakagawa. Parotid gland carcinoma: 32 years\u2019 experience from a single institute. J. Laryngol. Otol. 133(7):604\u2013609, 2019.","journal-title":"J. Laryngol. Otol."},{"key":"3510_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107000","volume":"161","author":"Y Gao","year":"2023","unstructured":"Gao, Y., Y. Dai, F. Liu, W. Chen, and L. Shi. An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal MRI. Comput. Biol. Med.161:107000, 2023.","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"3510_CR16","first-page":"4973","volume":"17","author":"WP Silva","year":"2016","unstructured":"Silva, W. P., R. T. Stramandinoli-Zanicotti, J. L. Schussel, G. H. Ramos, S. O. Ioshi, and L. M. Sassi. Accuracy, sensitivity and specificity of fine needle aspiration biopsy for salivary gland tumors: a retrospective study from 2006 to 2011. Asian Pac. J. Cancer Prev. 17(11):4973, 2016.","journal-title":"Asian Pac. J. Cancer Prev."},{"issue":"2","key":"3510_CR17","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1002\/hed.25503","volume":"41","author":"M Suzuki","year":"2019","unstructured":"Suzuki, M., R. Kawata, M. Higashino, S. Nishikawa, T. Terada, S.-I. Haginomori, Y. Kurisu, and Y. Hirose. Values of fine-needle aspiration cytology of parotid gland tumors: a review of 996 cases at a single institution. Head Neck. 41(2):358\u2013365, 2019.","journal-title":"Head Neck"},{"key":"3510_CR18","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1590\/1678-775720160214","volume":"24","author":"JK Gudmundsson","year":"2016","unstructured":"Gudmundsson, J. K., A. Ajan, and J. Abtahi. The accuracy of fine-needle aspiration cytology for diagnosis of parotid gland masses: a clinicopathological study of 114 patients. J. Appl. Oral Sci. 24:561\u2013567, 2016.","journal-title":"J. Appl. Oral Sci."},{"issue":"1","key":"3510_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1309\/AJCPOIE0CZNAT6SQ","volume":"136","author":"RL Schmidt","year":"2011","unstructured":"Schmidt, R. L., B. J. Hall, A. R. Wilson, and L. J. Layfield. A systematic review and meta-analysis of the diagnostic accuracy of fine-needle aspiration cytology for parotid gland lesions. Am. J. Clin. Pathol. 136(1):45\u201359, 2011.","journal-title":"Am. J. Clin. Pathol."},{"issue":"4","key":"3510_CR20","doi-asserted-by":"publisher","first-page":"20200391","DOI":"10.1259\/dmfr.20200391","volume":"50","author":"CZ Karaman","year":"2021","unstructured":"Karaman, C. Z., A. Tanyeri, R. Ozg\u00fcr, and V. S. \u00d6zt\u00fcrk. Parotid gland tumors: comparison of conventional and diffusion-weighted MRI findings with histopathological results. Dentomaxillofac. Radiol. 50(4):20200391, 2021.","journal-title":"Dentomaxillofac. Radiol."},{"key":"3510_CR21","first-page":"8192999","volume":"2022","author":"Y Wang","year":"2022","unstructured":"Wang, Y., W. Xie, S. Huang, M. Feng, X. Ke, Z. Zhong, L. Tang, et al. The diagnostic value of ultrasound-based deep learning in differentiating parotid gland tumors. J. Oncol. 2022:8192999, 2022.","journal-title":"J. Oncol."},{"key":"3510_CR22","doi-asserted-by":"publisher","DOI":"10.1590\/1678-7757-2017-0267","volume":"26","author":"T Mezei","year":"2018","unstructured":"Mezei, T., S. Mocan, A. Ormenisan, B. Bar\u00f3ti, and A. Iacob. The value of fine needle aspiration cytology in the clinical management of rare salivary gland tumors. J. Appl. Oral Sci.26:e20170267, 2018.","journal-title":"J. Appl. Oral Sci."},{"key":"3510_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.632104","volume":"11","author":"X Xia","year":"2021","unstructured":"Xia, X., B. Feng, J. Wang, Q. Hua, Y. Yang, L. Sheng, Y. Mou, and W. Hu. Deep learning for differentiating benign from malignant parotid lesions on MR images. Front. Oncol.11:632104, 2021.","journal-title":"Front. Oncol."},{"issue":"2","key":"3510_CR24","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.otorri.2018.03.005","volume":"70","author":"AR Lameiras","year":"2019","unstructured":"Lameiras, A. R., H. Estibeiro, P. Montalv\u00e3o, and M. Magalh\u00e3es. Diagnostic accuracy and utility of fine-needle aspiration cytology in therapeutic management of parotid gland tumours. Acta Otorrinolaringol. Esp\u00e3nola. 70(2):74\u201379, 2019.","journal-title":"Acta Otorrinolaringol. Esp\u00e3nola"},{"issue":"2","key":"3510_CR25","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1111\/coa.13038","volume":"43","author":"Y Shkedy","year":"2018","unstructured":"Shkedy, Y., U. Alkan, A. Mizrachi, T. Shochat, O. Dimitstein, S. Morgenstern, T. Shpitzer, and G. Bachar. Fine-needle aspiration cytology for parotid lesions, can we avoid surgery? Clin. Otolaryngol. 43(2):632\u2013637, 2018.","journal-title":"Clin. Otolaryngol."},{"key":"3510_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.919088","volume":"12","author":"Z Hu","year":"2022","unstructured":"Hu, Z., B. Wang, X. Pan, D. Cao, A. Gao, X. Yang, Y. Chen, and Z. Lin. Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images. Front. Oncol.12:919088, 2022.","journal-title":"Front. Oncol."},{"key":"3510_CR27","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/BF02489988","volume":"24","author":"N Ozawa","year":"2006","unstructured":"Ozawa, N., T. Okamura, K. Koyama, K. Nakayama, J. Kawabe, S. Shiomi, H. Yamane, and Y. Inoue. Retrospective review: usefulness of a number of imaging modalities including CT, MRI, technetium-99m pertechnetate scintigraphy, gallium-67 scintigraphy and F-18-FDG PET in the differentiation of benign from malignant parotid masses. Radiat. Med. 24:41\u201349, 2006.","journal-title":"Radiat. Med."},{"issue":"2","key":"3510_CR28","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1002\/lary.30154","volume":"133","author":"X Liu","year":"2023","unstructured":"Liu, X., Y. Pan, X. Zhang, Y. Sha, S. Wang, H. Li, and J. Liu. A deep learning model for classification of parotid neoplasms based on multimodal magnetic resonance image sequences. Laryngoscope. 133(2):327\u2013335, 2023.","journal-title":"Laryngoscope"},{"issue":"5","key":"3510_CR29","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.1002\/mp.13942","volume":"47","author":"CE Cardenas","year":"2020","unstructured":"Cardenas, C. E., A. S. Mohamed, J. Yang, M. Gooding, H. Veeraraghavan, J. Kalpathy-Cramer, S. P. Ng, Y. Ding, J. Wang, S. Y. Lai, et al. Head and neck cancer patient images for determining auto-segmentation accuracy in T2-weighted magnetic resonance imaging through expert manual segmentations. Med. Phys. 47(5):2317\u20132322, 2020.","journal-title":"Med. Phys."},{"issue":"2","key":"3510_CR30","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.otc.2015.10.004","volume":"49","author":"EC Kuan","year":"2016","unstructured":"Kuan, E. C., J.M.-S. Clair, and M. A. S. John. Evaluation of parotid lesions. Otolaryngol. Clin. N. Am. 49(2):313\u2013325, 2016.","journal-title":"Otolaryngol. Clin. N. Am."},{"issue":"7","key":"3510_CR31","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.3174\/ajnr.A2520","volume":"32","author":"A Christe","year":"2011","unstructured":"Christe, A., C. Waldherr, R. Hallett, P. Zbaeren, and H. Thoeny. MR imaging of parotid tumors: typical lesion characteristics in MR imaging improve discrimination between benign and malignant disease. Am. J. Neuroradiol. 32(7):1202\u20131207, 2011.","journal-title":"Am. J. Neuroradiol."},{"key":"3510_CR32","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.ejmp.2018.06.012","volume":"52","author":"D Mo\u010dnik","year":"2018","unstructured":"Mo\u010dnik, D., B. Ibragimov, L. Xing, P. Strojan, B. Likar, F. Pernu\u0161, and T. Vrtovec. Segmentation of parotid glands from registered CT and MR images. Phys. Med. 52:33\u201341, 2018.","journal-title":"Phys. Med."},{"issue":"6","key":"3510_CR33","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1002\/mp.13553","volume":"46","author":"N Tong","year":"2019","unstructured":"Tong, N., S. Gou, S. Yang, M. Cao, and K. Sheng. Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images. Med. Phys. 46(6):2669\u20132682, 2019.","journal-title":"Med. Phys."},{"issue":"2","key":"3510_CR34","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1097\/MOO.0000000000000782","volume":"30","author":"E G\u00fcnd\u00fcz","year":"2022","unstructured":"G\u00fcnd\u00fcz, E., O. F. Al\u00e7in, A. Kizilay, and C. Piazza. Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors. Curr. Opin. Otolaryngol. Head Neck Surg. 30(2):107\u2013113, 2022.","journal-title":"Curr. Opin. Otolaryngol. Head Neck Surg."},{"key":"3510_CR35","first-page":"1","volume":"35","author":"M Wo\u017aniak","year":"2021","unstructured":"Wo\u017aniak, M., J. Silka, and M. Wieczorek. Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput. Appl. 35:1\u201316, 2021.","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"3510_CR36","doi-asserted-by":"publisher","first-page":"19388","DOI":"10.1038\/s41598-020-76389-4","volume":"10","author":"H Matsuo","year":"2020","unstructured":"Matsuo, H., M. Nishio, T. Kanda, Y. Kojita, A. K. Kono, M. Hori, M. Teshima, N. Otsuki, K.-I. Nibu, and T. Murakami. Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI. Sci. Rep. 10(1):19388, 2020.","journal-title":"Sci. Rep."},{"issue":"1","key":"3510_CR37","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1007\/s12194-019-00552-4","volume":"13","author":"H Fujita","year":"2020","unstructured":"Fujita, H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol. Phys. Technol. 13(1):6\u201319, 2020.","journal-title":"Radiol. Phys. Technol."},{"issue":"4","key":"3510_CR38","doi-asserted-by":"publisher","first-page":"955","DOI":"10.18280\/ts.380405","volume":"38","author":"AB Slama","year":"2021","unstructured":"Slama, A. B., Z. Mbarki, H. Seddik, J. Marrakchi, S. Boukriba, and S. Labidi. Improving parotid gland tumor segmentation and classification using geometric active contour model and deep neural network framework. Trait. Signal. 38(4):955\u2013965, 2021.","journal-title":"Trait. Signal"},{"issue":"10","key":"3510_CR39","doi-asserted-by":"publisher","first-page":"4558","DOI":"10.1002\/mp.13147","volume":"45","author":"N Tong","year":"2018","unstructured":"Tong, N., S. Gou, S. Yang, D. Ruan, and K. Sheng. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks. Med. Phys. 45(10):4558\u20134567, 2018.","journal-title":"Med. Phys."},{"key":"3510_CR40","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/s12105-017-0795-0","volume":"11","author":"RR Seethala","year":"2017","unstructured":"Seethala, R. R., and G. Stenman. Update from the 4th edition of the world health organization classification of head and neck tumours: tumors of the salivary gland. Head Neck Pathol. 11:55\u201367, 2017.","journal-title":"Head Neck Pathol."},{"key":"3510_CR41","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, A., M. Schwier, T. Gass, T. Morgas, B. Haas, J. Klein, and H. K. Hahn. Comparison of different deep learning approaches for parotid gland segmentation from CT images. In: Medical Imaging 2018: Computer-Aided Diagnosis, 2018, vol. 10575. SPIE, 2018, pp. 289\u2013294.","DOI":"10.1117\/12.2292962"},{"issue":"5","key":"3510_CR42","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.3390\/electronics12051199","volume":"12","author":"Y Yu","year":"2023","unstructured":"Yu, Y., C. Wang, Q. Fu, R. Kou, F. Huang, B. Yang, T. Yang, and M. Gao. Techniques and challenges of image segmentation: a review. Electronics. 12(5):1199, 2023.","journal-title":"Electronics"},{"key":"3510_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102395","volume":"66","author":"F-P An","year":"2021","unstructured":"An, F.-P., J.-E. Liu, and J.-R. Wang. Medical image segmentation algorithm based on positive scaling invariant-self encoding CCA. Biomed. Signal Process. Control.66:102395, 2021.","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"3510_CR44","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.3390\/diagnostics12112765","volume":"12","author":"S-Y Huang","year":"2022","unstructured":"Huang, S.-Y., W.-L. Hsu, R.-J. Hsu, and D.-W. Liu. Fully convolutional network for the semantic segmentation of medical images: a survey. Diagnostics. 12(11):2765, 2022.","journal-title":"Diagnostics"},{"key":"3510_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, Z., L. Yang, and Y. Zheng. Translating and segmenting multimodal medical volumes with cycle-and shape-consistency generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 9242\u20139251.","DOI":"10.1109\/CVPR.2018.00963"},{"key":"3510_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, A., G. Balakrishnan, F. Durand, J. V. Guttag, and A. V. Dalca. Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 8543\u20138553.","DOI":"10.1109\/CVPR.2019.00874"},{"key":"3510_CR47","doi-asserted-by":"crossref","unstructured":"Baumgartner, C. F., K. C. Tezcan, K. Chaitanya, A. M. H\u00f6tker, U. J. Muehlematter, K. Schawkat, A. S. Becker, O. Donati, and E. Konukoglu. PHiSeg: capturing uncertainty in medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22, 2019. Springer, 2019, pp. 119\u2013127.","DOI":"10.1007\/978-3-030-32245-8_14"},{"key":"3510_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104498","volume":"81","author":"G Zhang","year":"2023","unstructured":"Zhang, G., C. Zheng, J. He, and S. Yi. PCT: pyramid convolutional transformer for parotid gland tumor segmentation in ultrasound images. Biomed. Signal Process. Control.81:104498, 2023.","journal-title":"Biomed. Signal Process. Control"},{"key":"3510_CR49","unstructured":"Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. In: Advances in Neural Information Processing Systems, 2017, vol. 30."},{"key":"3510_CR50","unstructured":"Dosovitskiy, A., L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16 \u00d7 16 words: Transformers for image recognition at scale, 2020. arXiv preprint arXiv:2010.11929."},{"key":"3510_CR51","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2023.3265384","author":"S Liu","year":"2023","unstructured":"Liu, S., J. Zhang, Z. Zhang, X. Cao, and T. S. Durrani. Integration transformer for ground-based cloud image segmentation. IEEE Trans. Geosci. Remote Sens. 2023. https:\/\/doi.org\/10.1109\/TGRS.2023.3265384.","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"3510_CR52","doi-asserted-by":"publisher","first-page":"581","DOI":"10.3390\/s23020581","volume":"23","author":"Z Xia","year":"2023","unstructured":"Xia, Z., and J. Kim. Enhancing mask transformer with auxiliary convolution layers for semantic segmentation. Sensors. 23(2):581, 2023.","journal-title":"Sensors"},{"issue":"2","key":"3510_CR53","first-page":"1559","volume":"135","author":"F Xu","year":"2023","unstructured":"Xu, F., H. Zhao, F. Hu, M. Shen, and Y. Wu. A road segmentation model based on mixture of the convolutional neural network and the transformer network. Comput. Model. Eng. Sci. 135(2):1559\u20131570, 2023.","journal-title":"Comput. Model. Eng. Sci."},{"issue":"4","key":"3510_CR54","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.3390\/app13042261","volume":"13","author":"M Yu","year":"2023","unstructured":"Yu, M., and F. Qin. Research on the applicability of transformer model in remote sensing image segmentation. Appl. Sci. 13(4):2261, 2023.","journal-title":"Appl. Sci."},{"issue":"2","key":"3510_CR55","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aca74c","volume":"68","author":"S Zheng","year":"2023","unstructured":"Zheng, S., J. Tan, C. Jiang, and L. Li. Automated multi-modal transformer network (AMTNet) for 3D medical images segmentation. Phys. Med. Biol.68(2):025014, 2023.","journal-title":"Phys. Med. Biol."},{"key":"3510_CR56","doi-asserted-by":"crossref","unstructured":"Long, J., E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"3510_CR57","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., P. Fischer, and T. Brox. U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18, 2015. Springer, 2015, pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3510_CR58","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., and M. S. Rahman. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 121:74\u201387, 2020.","journal-title":"Neural Netw."},{"key":"3510_CR59","doi-asserted-by":"crossref","unstructured":"Huang, C., H. Han, Q. Yao, S. Zhu, and S. K. Zhou. 3D U2-Net: a 3D universal UNet for multi-domain medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. Springer, 2019, pp. 291\u2013299","DOI":"10.1007\/978-3-030-32245-8_33"},{"key":"3510_CR60","doi-asserted-by":"crossref","unstructured":"Wang, Z., N. Zou, D. Shen, and S. Ji. Non-local u-nets for biomedical image segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020, vol. 34, pp. 6315\u20136322.","DOI":"10.1609\/aaai.v34i04.6100"},{"key":"3510_CR61","unstructured":"Isensee, F., J. Petersen, S. A. Kohl, P. F. J\u00e4ger, and K. H. Maier-Hein. nnU-Net: breaking the spell on successful medical image segmentation. 1(1\u20138):2, 2019. arXiv preprint arXiv:1904.08128."},{"key":"3510_CR62","unstructured":"Battaglia, P. W., J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al. Relational inductive biases, deep learning, and graph networks, 2018. arXiv preprint arXiv:1806.01261."},{"key":"3510_CR63","unstructured":"Chen, X., C.-J. Hsieh, and B. Gong. When vision transformers outperform ResNets without pre-training or strong data augmentations, 2021. arXiv preprint arXiv:2106.01548."},{"key":"3510_CR64","unstructured":"Steiner, A., A. Kolesnikov, X. Zhai, R. Wightman, J. Uszkoreit, and L. Beyer. How to train your ViT? Data, augmentation, and regularization in vision transformers, 2021. arXiv preprint arXiv:2106.10270."},{"key":"3510_CR65","doi-asserted-by":"crossref","unstructured":"Liu, Z., Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3510_CR66","unstructured":"Chen, J., Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A. L. Yuille, and Y. Zhou. TransUNet: transformers make strong encoders for medical image segmentation, 2021. arXiv preprint arXiv:2102.04306."},{"key":"3510_CR67","doi-asserted-by":"crossref","unstructured":"Valanarasu, J. M. J., P. Oza, I. Hacihaliloglu, and V. M. Patel. Medical transformer: gated axial-attention for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, 2021. Springer, 2021, pp. 36\u201346.","DOI":"10.1007\/978-3-030-87193-2_4"},{"key":"3510_CR68","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H. R. Roth, and D. Xu. UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, 2022, pp. 574\u2013584.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"3510_CR69","unstructured":"Yun, B., Y. Wang, J. Chen, H. Wang, W. Shen, and Q. Li. SPECTR: spectral transformer for hyperspectral pathology image segmentation, 2021. arXiv preprint arXiv:2103.03604."},{"key":"3510_CR70","doi-asserted-by":"crossref","unstructured":"Wang, W., C. Chen, M. Ding, H. Yu, S. Zha, and J. Li. TransBTS: multimodal brain tumor segmentation using transformer. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24, 2021. Springer, 2021, pp. 109\u2013119.","DOI":"10.1007\/978-3-030-87193-2_11"},{"key":"3510_CR71","doi-asserted-by":"crossref","unstructured":"Petit, O., N. Thome, C. Rambour, L. Themyr, T. Collins, and L. Soler. U-Net transformer: self and cross attention for medical image segmentation. In: Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12, 2021. Springer, 2021, pp. 267\u2013276.","DOI":"10.1007\/978-3-030-87589-3_28"},{"key":"3510_CR72","doi-asserted-by":"crossref","unstructured":"Cao, H., Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang. Swin-Unet: UNet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, 2022. Springer, 2022, pp. 205\u2013218.","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"4","key":"3510_CR73","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abd953","volume":"66","author":"X Dai","year":"2021","unstructured":"Dai, X., Y. Lei, T. Wang, A. H. Dhabaan, M. McDonald, J. J. Beitler, W. J. Curran, J. Zhou, T. Liu, and X. Yang. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy. Phys. Med. Biol.66(4):045021, 2021.","journal-title":"Phys. Med. Biol."},{"issue":"11","key":"3510_CR74","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abfce2","volume":"66","author":"T Wang","year":"2021","unstructured":"Wang, T., Y. Lei, J. Roper, B. Ghavidel, J. J. Beitler, M. McDonald, W. J. Curran, T. Liu, and X. Yang. Head and neck multi-organ segmentation on dual-energy CT using dual pyramid convolutional neural networks. Phys. Med. Biol.66(11):115008, 2021.","journal-title":"Phys. Med. Biol."},{"issue":"5","key":"3510_CR75","doi-asserted-by":"publisher","first-page":"13579","DOI":"10.1002\/acm2.13579","volume":"23","author":"D Kawahara","year":"2022","unstructured":"Kawahara, D., M. Tsuneda, S. Ozawa, H. Okamoto, M. Nakamura, T. Nishio, and Y. Nagata. Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients. J. Appl. Clin. Med. Phys. 23(5):13579, 2022.","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"3510_CR76","doi-asserted-by":"crossref","unstructured":"Lei, Y., J. Zhou, X. Dong, T. Wang, H. Mao, M. McDonald, W. J. Curran, T. Liu, and X. Yang. Multi-organ segmentation in head and neck MRI using U-faster-RCNN. In: Medical Imaging 2020: Image Processing, 2020, vol. 11313. SPIE, 2020, pp. 826\u2013831.","DOI":"10.1117\/12.2549596"},{"key":"3510_CR77","doi-asserted-by":"crossref","unstructured":"Wu, K., H. Peng, M. Chen, J. Fu, and H. Chao. Rethinking and improving relative position encoding for vision transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 10033\u201310041.","DOI":"10.1109\/ICCV48922.2021.00988"},{"key":"3510_CR78","doi-asserted-by":"crossref","unstructured":"Chen, X., S. Xie, and K. He. An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, 2021, pp. 9640\u20139649.","DOI":"10.1109\/ICCV48922.2021.00950"},{"key":"3510_CR79","doi-asserted-by":"crossref","unstructured":"Zhou, Z., M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang. UNet++: a nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, 2018. Springer, 2018, pp. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"3510_CR80","doi-asserted-by":"publisher","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","volume":"8","author":"T Fan","year":"2020","unstructured":"Fan, T., G. Wang, Y. Li, and H. Wang. MA-Net: a multi-scale attention network for liver and tumor segmentation. IEEE Access. 8:179656\u2013179665, 2020.","journal-title":"IEEE Access"},{"key":"3510_CR81","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and E. Culurciello. LinkNet: exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE Visual Communications and Image Processing (VCIP), 2017. IEEE, 2017, pp. 1\u20134.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"3510_CR82","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., P. Doll\u00e1r, R. Girshick, K. He, B. Hariharan, and S. Belongie. Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"3510_CR83","doi-asserted-by":"crossref","unstructured":"Zhao, H., J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2881\u20132890.","DOI":"10.1109\/CVPR.2017.660"},{"key":"3510_CR84","unstructured":"Li, H., P. Xiong, J. An, and L. Wang. Pyramid attention network for semantic segmentation, 2018. arXiv preprint arXiv:1805.10180."},{"key":"3510_CR85","unstructured":"Chen, L.-C., G. Papandreou, F. Schroff, and H. Adam. Rethinking Atrous convolution for semantic image segmentation, 2017. arXiv preprint arXiv:1706.05587."},{"key":"3510_CR86","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. Encoder\u2013decoder with Atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"3510_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102444","volume":"79","author":"X Chen","year":"2022","unstructured":"Chen, X., X. Wang, K. Zhang, K.-M. Fung, T. C. Thai, K. Moore, R. S. Mannel, H. Liu, B. Zheng, and Y. Qiu. Recent advances and clinical applications of deep learning in medical image analysis. Med. Image Anal.79:102444, 2022.","journal-title":"Med. Image Anal."},{"key":"3510_CR88","doi-asserted-by":"publisher","first-page":"93145","DOI":"10.1109\/ACCESS.2020.2993887","volume":"8","author":"H Tang","year":"2020","unstructured":"Tang, H., and Z. Hu. Research on medical image classification based on machine learning. IEEE Access. 8:93145\u201393154, 2020.","journal-title":"IEEE Access"},{"issue":"1","key":"3510_CR89","doi-asserted-by":"publisher","first-page":"6004","DOI":"10.1038\/s41467-020-19817-3","volume":"11","author":"D Li","year":"2020","unstructured":"Li, D., J. R. Bledsoe, Y. Zeng, W. Liu, Y. Hu, K. Bi, A. Liang, and S. Li. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat. Commun. 11(1):6004, 2020.","journal-title":"Nat. Commun."},{"key":"3510_CR90","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101819","volume":"67","author":"Y Chen","year":"2021","unstructured":"Chen, Y., D. Li, X. Zhang, J. Jin, and Y. Shen. Computer aided diagnosis of thyroid nodules based on the devised small-datasets multi-view ensemble learning. Med. Image Anal.67:101819, 2021.","journal-title":"Med. Image Anal."}],"container-title":["Annals of Biomedical Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-024-03510-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10439-024-03510-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10439-024-03510-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T19:01:34Z","timestamp":1720983694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10439-024-03510-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,1]]},"references-count":90,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8]]}},"alternative-id":["3510"],"URL":"https:\/\/doi.org\/10.1007\/s10439-024-03510-3","relation":{},"ISSN":["0090-6964","1573-9686"],"issn-type":[{"value":"0090-6964","type":"print"},{"value":"1573-9686","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,1]]},"assertion":[{"value":"29 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2024","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 that there are no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All procedures in this study were approved by the Ethics Committee of the Affiliated Stomatological Hospital of China Medical University. The research was conducted in accordance with the principles embodied in the Declaration of Helsinki and in accordance with local statutory requirements.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}