{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T12:43:24Z","timestamp":1763037804063,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164422"},{"type":"electronic","value":"9783031164439"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16443-9_6","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"55-65","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Thoracic Lymph Node Segmentation in\u00a0CT Imaging via\u00a0Lymph Node Station Stratification and\u00a0Size Encoding"],"prefix":"10.1007","author":[{"given":"Dazhou","family":"Guo","sequence":"first","affiliation":[]},{"given":"Jia","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Puyang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhuotun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Dandan","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[]},{"given":"Le","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Tsung-Ying","family":"Ho","sequence":"additional","affiliation":[]},{"given":"Xianghua","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Dakai","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"issue":"2","key":"6_CR1","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/TMI.2011.2168234","volume":"31","author":"A Barbu","year":"2011","unstructured":"Barbu, A., Suehling, M., Xu, X., Liu, D., Zhou, S.K., Comaniciu, D.: Automatic detection and segmentation of lymph nodes from CT data. IEEE Trans. Med. Imaging 31(2), 240\u2013250 (2011)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"6_CR2","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1007\/s11548-019-01948-8","volume":"14","author":"D Bouget","year":"2019","unstructured":"Bouget, D., J\u00f8rgensen, A., Kiss, G., Leira, H.O., Lang\u00f8, T.: Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging. Int. J. Comput. Assist. Radiol. Surg. 14(6), 977\u2013986 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Bouget, D., Pedersen, A., Vanel, J., Leira, H.O., Lang\u00f8, T.: Mediastinal lymph nodes segmentation using 3d convolutional neural network ensembles and anatomical priors guiding. arXiv preprint arXiv:2102.06515 (2021)","DOI":"10.1080\/21681163.2022.2043778"},{"key":"6_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1007\/978-3-030-59728-3_75","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C-H Chao","year":"2020","unstructured":"Chao, C.-H., et al.: Lymph node gross tumor volume detection in oncology imaging via relationship learning using graph neural network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 772\u2013782. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_75"},{"issue":"3","key":"6_CR5","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/j.pan.2017.03.008","volume":"17","author":"SB Choi","year":"2017","unstructured":"Choi, S.B., Han, H.J., Park, P., Kim, W.B., Song, T.J., Choi, S.Y.: Systematic review of the clinical significance of lymph node micrometastases of pancreatic adenocarcinoma following surgical resection. Pancreatology 17(3), 342\u2013349 (2017)","journal-title":"Pancreatology"},{"issue":"1","key":"6_CR6","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.ejcts.2005.10.002","volume":"29","author":"AJ De Langen","year":"2006","unstructured":"De Langen, A.J., Raijmakers, P., Riphagen, I., Paul, M.A., Hoekstra, O.S.: The size of mediastinal lymph nodes and its relation with metastatic involvement: a meta-analysis. Eur. J. Cardiothorac. Surg. 29(1), 26\u201329 (2006)","journal-title":"Eur. J. Cardiothorac. Surg."},{"issue":"1","key":"6_CR7","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.media.2011.05.005","volume":"16","author":"M Feuerstein","year":"2012","unstructured":"Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., Mori, K.: Mediastinal atlas creation from 3-d chest computed tomography images: application to automated detection and station mapping of lymph nodes. Med. Image Anal. 16(1), 63\u201374 (2012)","journal-title":"Med. Image Anal."},{"issue":"2","key":"6_CR8","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.media.2012.11.001","volume":"17","author":"J Feulner","year":"2013","unstructured":"Feulner, J., Zhou, S.K., Hammon, M., Hornegger, J., Comaniciu, D.: Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior. Med. Image Anal. 17(2), 254\u2013270 (2013)","journal-title":"Med. Image Anal."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Feulner, J., Zhou, S.K., Huber, M., Hornegger, J., Comaniciu, D., Cavallaro, A.: Lymph node detection in 3-d chest CT using a spatial prior probability. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2926\u20132932. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5540034"},{"issue":"8","key":"6_CR10","doi-asserted-by":"publisher","first-page":"706","DOI":"10.1097\/JTO.0b013e31812f3c1a","volume":"2","author":"P Goldstraw","year":"2007","unstructured":"Goldstraw, P., et al.: The IASLC lung cancer staging project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM classification of malignant tumours. J. Thorac. Oncol. 2(8), 706\u2013714 (2007)","journal-title":"J. Thorac. Oncol."},{"key":"6_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-87240-3_1","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"D Guo","year":"2021","unstructured":"Guo, D., et al.: DeepStationing: thoracic lymph node station parsing in CT scans using anatomical context encoding and key organ auto-search. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 3\u201312. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_1"},{"key":"6_CR12","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18, 203\u2013211 (2021)","journal-title":"Nat. Meth."},{"issue":"1","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-021-00599-z","volume":"21","author":"AI Iuga","year":"2021","unstructured":"Iuga, A.I., et al.: Automated detection and segmentation of thoracic lymph nodes from CT using 3d foveal fully convolutional neural networks. BMC Med. Imaging 21(1), 1\u201312 (2021)","journal-title":"BMC Med. Imaging"},{"issue":"3","key":"6_CR14","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1109\/JBHI.2020.3008759","volume":"25","author":"Z Li","year":"2020","unstructured":"Li, Z., Xia, Y.: Deep reinforcement learning for weakly-supervised lymph node segmentation in CT images. IEEE J. Biomed. Health Inform. 25(3), 774\u2013783 (2020)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"7","key":"6_CR15","doi-asserted-by":"publisher","first-page":"4362","DOI":"10.1118\/1.4954009","volume":"43","author":"J Liu","year":"2016","unstructured":"Liu, J., et al.: Mediastinal lymph node detection and station mapping on chest CT using spatial priors and random forest. Med. Phys. 43(7), 4362\u20134374 (2016)","journal-title":"Med. Phys."},{"key":"6_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1007\/978-3-319-46723-8_45","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"I Nogues","year":"2016","unstructured":"Nogues, I., et al.: Automatic lymph node cluster segmentation using holistically-nested neural networks and\u00a0structured optimization in CT images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 388\u2013397. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_45"},{"issue":"5","key":"6_CR17","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TMI.2015.2482920","volume":"35","author":"HR Roth","year":"2016","unstructured":"Roth, H.R., et al.: Improving computer-aided detection using convolutional neural networks and random view aggregation. IEEE Trans. Med. Imaging 35(5), 1170\u20131181 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"6_CR18","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1001\/archotol.128.7.751","volume":"128","author":"KT Roubbins","year":"2002","unstructured":"Roubbins, K.T., et al.: Neck dissection classification update: revisions proposed by the American head and neck society and the American academy of otolaryngology-head and neck surgery. Arch. Otolaryngol. Head Neck Surg. 128(7), 751\u2013758 (2002)","journal-title":"Arch. Otolaryngol. Head Neck Surg."},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Rusch, V.W., Asamura, H., Watanabe, H., Giroux, D.J., Rami-Porta, R., Goldstraw, P.: The IASLC lung cancer staging project: a proposal for a new international lymph node map in the forthcoming seventh edition of the TNM classification for lung cancer. J. Thorac. Oncol. 4(5), 568\u2013577 (2009)","DOI":"10.1097\/JTO.0b013e3181a0d82e"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Schwartz, L., et al.: Evaluation of lymph nodes with RECIST 1.1. Eur. J. Cancer 45(2), 261\u2013267 (2009)","DOI":"10.1016\/j.ejca.2008.10.028"},{"issue":"3","key":"6_CR21","doi-asserted-by":"publisher","first-page":"195","DOI":"10.3322\/caac.21217","volume":"64","author":"P Stanley Leong","year":"2014","unstructured":"Stanley Leong, P., Tseng, W.W.: Micrometastatic cancer cells in lymph nodes, bone marrow, and blood: clinical significance and biologic implications. Cancer J. Clin. (CA) 64(3), 195\u2013206 (2014)","journal-title":"Cancer J. Clin. (CA)"},{"issue":"2","key":"6_CR22","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1148\/radiology.182.2.1732943","volume":"182","author":"TC McLoud","year":"1992","unstructured":"McLoud, T.C., et al.: Bronchogenic carcinoma: analysis of staging in the mediastinum with CT by correlative lymph node mapping and sampling. Radiology 182(2), 319\u2013323 (1992)","journal-title":"Radiology"},{"issue":"3","key":"6_CR23","first-page":"230","volume":"6","author":"MD Ter\u00e1n","year":"2014","unstructured":"Ter\u00e1n, M.D., Brock, M.V.: Staging lymph node metastases from lung cancer in the mediastinum. J. Thorac. Dis. 6(3), 230 (2014)","journal-title":"J. Thorac. Dis."},{"issue":"10","key":"6_CR24","doi-asserted-by":"publisher","first-page":"2759","DOI":"10.1109\/TMI.2020.3047598","volume":"40","author":"K Yan","year":"2020","unstructured":"Yan, K., et al.: Learning from multiple datasets with heterogeneous and partial labels for universal lesion detection in CT. IEEE Trans. Med. Imaging 40(10), 2759\u20132770 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"6_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1007\/978-3-030-59728-3_73","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Z Zhu","year":"2020","unstructured":"Zhu, Z., et al.: Lymph node gross tumor volume detection and segmentation via distance-based gating using 3d CT\/PET imaging in radiotherapy. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 753\u2013762. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_73"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16443-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:43:44Z","timestamp":1709829824000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"574","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}