{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:54:10Z","timestamp":1768902850690,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["No.2023M742568"],"award-info":[{"award-number":["No.2023M742568"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00530-024-01465-y","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:02:58Z","timestamp":1726218178000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic lymph node segmentation using deep parallel squeeze &amp; excitation and attention Unet"],"prefix":"10.1007","volume":"30","author":[{"given":"Zhaorui","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caiyin","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"1465_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F.: Global Cancer statistics 2020: GLOBOCAN estimates of incidence and Mortality Worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209\u2013249 (2021)","journal-title":"CA Cancer J. Clin."},{"key":"1465_CR2","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1038\/s41572-020-00224-3","volume":"6","author":"DE Johnson","year":"2020","unstructured":"Johnson, D.E., Burtness, B., Leemans, C.R., Lui, V.W.Y., Bauman, J.E., Grandis, J.R.: Head and neck squamous cell carcinoma. Nat. Rev. Dis. Primers. 6, 92 (2020)","journal-title":"Nat. Rev. Dis. Primers"},{"issue":"7","key":"1465_CR3","doi-asserted-by":"publisher","first-page":"620","DOI":"10.1111\/jop.13333","volume":"51","author":"S Feng","year":"2022","unstructured":"Feng, S., Yuan, W., Sun, Z., et al.: SPP1 as a key gene in the lymph node metastasis and a potential predictor of poor prognosis in head and neck carcinoma. J. Oral Pathol. Med. 51(7), 620\u2013629 (2022)","journal-title":"J. Oral Pathol. Med."},{"issue":"4","key":"1465_CR4","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1158\/0008-5472.CAN-10-3277","volume":"71","author":"K Kawada","year":"2011","unstructured":"Kawada, K., Makoto, M.: Taketo. Significance and mechanism of lymph node metastasis in cancer progression. Cancer Res. 71(4), 1214\u20131218 (2011)","journal-title":"Cancer Res."},{"issue":"2","key":"1465_CR5","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1002\/hed.27239","volume":"45","author":"C Giannitto","year":"2023","unstructured":"Giannitto, C., Mercante, G., Ammirabile, A., et al.: Radiomics-based machine learning for the diagnosis of lymph node metastases in patients with head and neck cancer: Systematic review. Head Neck. 45(2), 482\u2013491 (2023)","journal-title":"Head Neck"},{"key":"1465_CR6","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.radonc.2019.04.006","volume":"137","author":"J Van Der Veen","year":"2019","unstructured":"Van Der Veen, J., Gulyban, A., Nuyts, S.: Interobserver variability in delineation of target volumes in head and neck cancer. Radiother. Oncol. 137, 9\u201315 (2019)","journal-title":"Radiother. Oncol."},{"issue":"2","key":"1465_CR7","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1002\/ima.22371","volume":"30","author":"Y Ma","year":"2020","unstructured":"Ma, Y., Peng, Y.: Lymph node detection method based on multisource transfer learning and convolutional neural network. Int. J. Imaging Syst. Technol. 30(2), 298\u2013310 (2020)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"1465_CR8","doi-asserted-by":"crossref","unstructured":"Singh, J., Iwahori, Y., Bhuyan, M., Usami, H., Oshiro, T., Shimizu, Y.: Mediastinal lymph node detection using deep learning. In: Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, Valletta, Malta, pp 159\u2013166 (2020)","DOI":"10.5220\/0008948801590166"},{"key":"1465_CR9","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1148\/radiol.220329","volume":"306","author":"Y Bian","year":"2023","unstructured":"Bian, Y., Zheng, Z., Fang, X., Jiang, H., Zhu, M., Yu, J., Zhao, H., Zhang, L., Yao, J., Lu, L., Lu, J., Shao, C.: Artificial intelligence to predict lymph node metastasis at CT in pancreatic ductal adenocarcinoma. Radiology. 306, 160\u2013169 (2023)","journal-title":"Radiology"},{"key":"1465_CR10","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1007\/s00330-022-09153-z","volume":"33","author":"X Ma","year":"2023","unstructured":"Ma, X., Xia, L., Chen, J., et al.: Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model. Eur. Radiol. 33, 1949\u20131962 (2023)","journal-title":"Eur. Radiol."},{"key":"1465_CR11","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1007\/s11548-022-02822-w","volume":"18","author":"Y Manjunatha","year":"2023","unstructured":"Manjunatha, Y., Sharma, V., Iwahori, Y., Bhuyan, M.K., Wang, A., Ouchi, A., Shimizu, Y.: Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network. Int. J. CARS. 18, 723\u2013732 (2023)","journal-title":"Int. J. CARS"},{"key":"1465_CR12","doi-asserted-by":"publisher","first-page":"2054","DOI":"10.1002\/mp.12844","volume":"45","author":"Y Tan","year":"2018","unstructured":"Tan, Y., Lu, L., Bonde, A., Wang, D., Qi, J., Schwartz, L.H., Zhao, B.: Lymph node segmentation by dynamic programming and active contours. Med. Phys. 45, 2054\u20132062 (2018). https:\/\/doi.org\/10.1002\/mp.12844","journal-title":"Med. Phys."},{"key":"1465_CR13","doi-asserted-by":"publisher","unstructured":"Xu, G., Cao, H., Dong, Y., Yue, C., Li, K., Tong, Y.: Focal loss function based DeepLabv3\u2009+\u2009for pathological lymph node segmentation on PET\/CT. In: Proceedings of the 2020 2nd International Conference on Intelligent Medicine and Image Processing, ACM, Tianjin China, pp. 24\u201328. (2020). https:\/\/doi.org\/10.1145\/3399637.3399651","DOI":"10.1145\/3399637.3399651"},{"key":"1465_CR14","first-page":"212","volume-title":"HNT-AI: An Automatic Segmentation Framework for head and neck Primary Tumors and Lymph Nodes in FDG-PET\/CT images.3D Head and Neck Tumor Segmentation in PET\/CT Challenge","author":"Z Salahuddin","year":"2022","unstructured":"Salahuddin, Z., Chen, Y., Zhong, X., et al.: HNT-AI: an automatic segmentation framework for head and neck primary tumors and lymph nodes in FDG-PET\/CT images.3D head and neck tumor segmentation in PET\/CT challenge, pp. 212\u2013220. Springer Nature Switzerland, Cham (2022)"},{"key":"1465_CR15","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/LRA.2019.2900854","volume":"4","author":"M Islam","year":"2019","unstructured":"Islam, M., Atputharuban, D.A., Ramesh, R., Ren, H.: Real-time instrument segmentation in robotic surgery using Auxiliary supervised Deep Adversarial Learning. IEEE Robot Autom. Lett. 4, 2188\u20132195 (2019)","journal-title":"IEEE Robot Autom. Lett."},{"key":"1465_CR16","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization (2017). http:\/\/arxiv.org\/abs\/1607.08022"},{"key":"1465_CR17","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. Proc. icml. 30(1), 3 (2013)"},{"key":"1465_CR18","doi-asserted-by":"publisher","first-page":"1115258","DOI":"10.3389\/fonc.2023.1115258","volume":"13","author":"T Weissmann","year":"2023","unstructured":"Weissmann, T., Huang, Y., Fischer, S., Roesch, J., Mansoorian, S., Ayala Gaona, H., Gostian, A.-O., Hecht, M., Lettmaier, S., Deloch, L., Frey, B., Gaipl, U.S., Distel, L.V., Maier, A., Iro, H., Semrau, S., Bert, C., Fietkau, R., Putz, F.: Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy. Front. Oncol. 13, 1115258 (2023)","journal-title":"Front. Oncol."},{"key":"1465_CR19","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.3390\/s23104688","volume":"23","author":"L Nanni","year":"2023","unstructured":"Nanni, L., Fantozzi, C., Loreggia, A., Lumini, A.: Ensembles of Convolutional Neural Networks and transformers for Polyp Segmentation. Sensors. 23, 4688 (2023)","journal-title":"Sensors"},{"key":"1465_CR20","unstructured":"Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807\u2013814"},{"issue":"3","key":"1465_CR21","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/0893-6080(91)90075-G","volume":"4","author":"Y Ito","year":"1991","unstructured":"Ito, Y.: Representation of functions by superpositions of a step or sigmoid function and theirapplications to neural network theory. Neural Netw. 4(3), 385\u2013394 (1991)","journal-title":"Neural Netw."},{"key":"1465_CR22","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: LearningDense volumetric segmentation from sparse annotation (2016). http:\/\/arxiv.org\/abs\/1606.06650","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"1465_CR23","doi-asserted-by":"publisher","first-page":"102167","DOI":"10.1016\/j.compmedimag.2022.102167","volume":"104","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Ruan, S., Hu, H.: A literature survey of MR-based brain tumor segmentation withmissing modalities. Comput. Med. Imaging Graph. 104, 102167 (2023)","journal-title":"Comput. Med. Imaging Graph."},{"key":"1465_CR24","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1109\/34.232073","volume":"15","author":"DP Huttenlocher","year":"1993","unstructured":"Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.J.: Comparing images using theHausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell. 15, 850\u2013863 (1993)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1465_CR25","doi-asserted-by":"publisher","first-page":"108159","DOI":"10.1016\/j.patcog.2021.108159","volume":"121","author":"X Jin","year":"2022","unstructured":"Jin, X., Xie, Y., Wei, X.-S., Zhao, B.-R., Chen, Z.-M.: Tan X.Delving deep into spatial pooling for squeeze-and-excitation networks. Pattern Recogn. 121, 108159 (2022)","journal-title":"Pattern Recogn."},{"key":"1465_CR26","doi-asserted-by":"publisher","first-page":"104699","DOI":"10.1016\/j.compbiomed.2021.104699","volume":"136","author":"H Zunair","year":"2021","unstructured":"Zunair, H., Ben Hamza, A., Sharp: U-Net: Depthwise convolutional network for biomedical image segmentation. Comput. Biol. Med. 136, 104699 (2021)","journal-title":"Comput. Biol. Med."},{"key":"1465_CR27","doi-asserted-by":"publisher","first-page":"5549","DOI":"10.1038\/s41598-022-09514-0","volume":"12","author":"M Dehghani","year":"2022","unstructured":"Dehghani, M., Trojovsk\u00fd, P.: Hybrid leader based optimization: A new stochastic optimization algorithm for solving optimization applications. Sci. Rep. 12, 5549 (2022)","journal-title":"Sci. Rep."},{"key":"1465_CR28","doi-asserted-by":"crossref","unstructured":"Peng, T., Zhao, J., Wang, J., Interpretable mathematical model-guided ultrasound prostateContour extraction using data mining techniques. In: 2021 IEEE International Conference onBioinformatics and, Biomedicine: (BIBM), Houston, TX, USA, pp. 1037\u20131044 (2021)","DOI":"10.1109\/BIBM52615.2021.9669419"},{"key":"1465_CR29","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D.: Attention U-Net: learning where to look for the pancreas (2018). http:\/\/arxiv.org\/abs\/1804.03999"},{"key":"1465_CR30","doi-asserted-by":"publisher","first-page":"116873","DOI":"10.1016\/j.eswa.2022.116873","volume":"198","author":"T Peng","year":"2022","unstructured":"Peng, T., Gu, Y., Ye, Z., Cheng, X., Wang, J.: Automatic and explainability-guided multi-site lung detection in chest X-ray images. Expert Syst. Appl. 198, 116873 (2022)","journal-title":"Expert Syst. Appl."}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01465-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01465-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01465-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T18:13:07Z","timestamp":1730139187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01465-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,13]]},"references-count":30,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1465"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01465-y","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,13]]},"assertion":[{"value":"5 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"270"}}