{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T09:38:43Z","timestamp":1782207523404,"version":"3.54.5"},"publisher-location":"Wiesbaden","reference-count":14,"publisher":"Springer Fachmedien Wiesbaden","isbn-type":[{"value":"9783658416560","type":"print"},{"value":"9783658416577","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-658-41657-7_36","type":"book-chapter","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T07:02:48Z","timestamp":1685602968000},"page":"165-170","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Lung Nodule Segmentation in CT Imaging using an Improved 3D-Res2Unet"],"prefix":"10.1007","author":[{"given":"Pavan","family":"Tummala","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Hille","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvia","family":"Saalfeld","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7\u201334.","DOI":"10.3322\/caac.21551"},{"key":"36_CR2","doi-asserted-by":"crossref","unstructured":"Kamble B, Sahu SP, Doriya R. A review on lung and nodule segmentation techniques. Advances in Data and Information Sciences: Proceedings of ICDIS 2019. 2020:555\u201365.","DOI":"10.1007\/978-981-15-0694-9_52"},{"key":"36_CR3","doi-asserted-by":"crossref","unstructured":"Carvalho Filho AO de, Sampaio WB de, Silva AC, Paiva AC de, Nunes RA, Gattass M. Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index. Artif Intell Med. 2014;60(3):165\u201377.","DOI":"10.1016\/j.artmed.2013.11.002"},{"key":"36_CR4","doi-asserted-by":"crossref","unstructured":"Pezzano G, Ripoll VR, Radeva P. CoLe-CNN: context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Comput Methods Programs Biomed. 2021;198:105792.","DOI":"10.1016\/j.cmpb.2020.105792"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Xiao Z, Liu B, Geng L, Zhang F, Liu Y. Segmentation of lung nodules using improved 3D-UNet neural network. Symmetry (Basel). 2020;12(11):1787.","DOI":"10.3390\/sym12111787"},{"key":"36_CR6","unstructured":"Keetha NV, Annavarapu CSR et al. U-Det: a modified U-Net architecture with bidirectional feature network for lung nodule segmentation. arXiv preprint arXiv:2003.09293. 2020."},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Huang X, Sun W, Tseng TLB, Li C, Qian W. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Computerized Medical Imaging and Graphics. 2019;74:25\u201336.","DOI":"10.1016\/j.compmedimag.2019.02.003"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Khosravan N, Bagci U. Semi-supervised multi-task learning for lung cancer diagnosis. 2018 40th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE. 2018:710\u20133.","DOI":"10.1109\/EMBC.2018.8512294"},{"key":"36_CR9","unstructured":"LUng Nodule Analysis 2016 (LUNA16). https:\/\/luna16.grand- challenge.org\/Home\/. Accessed: 2010-09-30."},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38(2):915\u201331.","DOI":"10.1118\/1.3528204"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. CoRR. 2015;abs\/1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Gao SH, Cheng MM, Zhao K, Zhang XY, Yang MH, Torr P. Res2Net: a new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021;43(2):652\u201362.","DOI":"10.1109\/TPAMI.2019.2938758"},{"key":"36_CR13","unstructured":"Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings. 2010:249\u201356."},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. 2016.","DOI":"10.1007\/978-3-319-46723-8_49"}],"container-title":["Informatik aktuell","Bildverarbeitung f\u00fcr die Medizin 2023"],"original-title":[],"language":"de","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-658-41657-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T10:04:53Z","timestamp":1707127493000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-658-41657-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783658416560","9783658416577"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-658-41657-7_36","relation":{},"ISSN":["1431-472X","2628-8958"],"issn-type":[{"value":"1431-472X","type":"print"},{"value":"2628-8958","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"2 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"BVM Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Braunschweig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deutschland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bvm2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.bvm-workshop.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}