{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:08:33Z","timestamp":1778825313806,"version":"3.51.4"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030009458","type":"print"},{"value":"9783030009465","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-00946-5_22","type":"book-chapter","created":{"date-parts":[[2018,9,11]],"date-time":"2018-09-11T09:00:03Z","timestamp":1536656403000},"page":"215-224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi-structure Segmentation from Partially Labeled Datasets. Application to Body Composition Measurements on CT Scans"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9694-0766","authenticated-orcid":false,"given":"Germ\u00e1n","family":"Gonz\u00e1lez","sequence":"first","affiliation":[]},{"given":"George R.","family":"Washko","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-1996","authenticated-orcid":false,"given":"Ra\u00fal","family":"San Jos\u00e9 Est\u00e9par","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,9,12]]},"reference":[{"key":"22_CR1","unstructured":"Kayalibay, B., Jensen, G., van der Smagt, P.: CNN-based segmentation of medical imaging data. arXiv preprint arXiv:1701.03056 (2017)"},{"key":"22_CR2","unstructured":"Cai, J., Lu, L., Xie, Y., Xing, F., Yang, L.: Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. arXiv preprint arXiv:1707.04912 (2017)"},{"key":"22_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/978-3-319-66179-7_33","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"L Fidon","year":"2017","unstructured":"Fidon, L., et al.: Scalable multimodal convolutional networks for brain tumour segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 285\u2013293. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_33"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.11.005","volume":"44","author":"M Drozdzal","year":"2018","unstructured":"Drozdzal, M., Chartrand, G., Vorontsov, E.: Learning normalized inputs for iterative estimation in medical image segmentation. Med. Image Anal. 44, 1\u201313 (2018)","journal-title":"Med. Image Anal."},{"key":"22_CR5","unstructured":"Roth, H.R., et al.: Hierarchical 3D fully convolutional networks for multi-organ segmentation. arXiv preprint arXiv:1704.06382 (2017)"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Fidon, L., et al.: Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. arXiv preprint arXiv:1707.00478 (2017)","DOI":"10.1007\/978-3-319-75238-9_6"},{"key":"22_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_28"},{"key":"22_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"22_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/978-3-319-46976-8_19","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"M Drozdzal","year":"2016","unstructured":"Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S., Pal, C.: The importance of skip connections in biomedical image segmentation. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA -2016. LNCS, vol. 10008, pp. 179\u2013187. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_19"},{"issue":"3","key":"22_CR10","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1513\/AnnalsATS.201307-229OC","volume":"11","author":"MLN McDonald","year":"2014","unstructured":"McDonald, M.L.N., et al.: Quantitative computed tomography measures of pectoralis muscle area and disease severity in chronic obstructive pulmonary disease. A cross-sectional study. Ann. Am. Thorac. Soc. 11(3), 326\u2013334 (2014)","journal-title":"Ann. Am. Thorac. Soc."},{"issue":"1","key":"22_CR11","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1158\/1055-9965.EPI-15-1067","volume":"26","author":"CM Kinsey","year":"2017","unstructured":"Kinsey, C.M., San Jos\u00e9e Est\u00e9epar, R., Van der Velden, J., Cole, B.F., Christiani, D.C., Washko, G.R.: Lower pectoralis muscle area is associated with a worse overall survival in non- small cell lung cancer. Cancer Epidemiol., Biomark. Prev.: Publ. Am. Assoc. Cancer Res., Cosponsored Am. Soc. Prev. Oncol. 26(1), 38\u201343 (2017)","journal-title":"Cancer Epidemiol., Biomark. Prev.: Publ. Am. Assoc. Cancer Res., Cosponsored Am. Soc. Prev. Oncol."},{"key":"22_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-319-13972-2_4","volume-title":"Medical Computer Vision: Algorithms for Big Data","author":"R Harmouche","year":"2014","unstructured":"Harmouche, R., Ross, J.C., Washko, G.R., San Jos\u00e9 Est\u00e9par, R.: Pectoralis muscle segmentation on CT images based on bayesian graph cuts with a subject-tailored atlas. In: Menze, B., et al. (eds.) MCV 2014. LNCS, vol. 8848, pp. 34\u201344. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-13972-2_4"},{"key":"22_CR13","unstructured":"Moreta-Martinez, R., Onieva-Onieva, J., Pascau, J., San Jose Est\u00e9par, R.: Pectoralis muscle and subcutaneous adipose tissue segmentation on CT images based on convolutional networks. In: Computer Assisted Radiology and Surgery. Springer (2017)"},{"key":"22_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D u-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"issue":"1","key":"22_CR15","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3109\/15412550903499522","volume":"7","author":"EA Regan","year":"2010","unstructured":"Regan, E.A., et al.: Genetic epidemiology of copd (copdgene) study design. COPD 7(1), 32\u201343 (2010)","journal-title":"COPD"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"}],"container-title":["Lecture Notes in Computer Science","Image Analysis for Moving Organ, Breast, and Thoracic Images"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00946-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T00:05:59Z","timestamp":1694390759000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-00946-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030009458","9783030009465"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00946-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 September 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Thoracic Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tia2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.lungworkshop.org\/2018\/","order":11,"name":"conference_url","label":"Conference URL","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"21","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":"20","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":"2","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":"95% - 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":"2-3","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":"n\/a","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}