{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:56:45Z","timestamp":1742925405319,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164309"},{"type":"electronic","value":"9783031164316"}],"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\/978-3-031-16431-6_66","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T21:02:58Z","timestamp":1663189378000},"page":"696-706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rib Suppression in\u00a0Digital Chest Tomosynthesis"],"prefix":"10.1007","author":[{"given":"Yihua","family":"Sun","sequence":"first","affiliation":[]},{"given":"Qingsong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Yuanyuan","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Jianji","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yi","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Hongen","family":"Liao","sequence":"additional","affiliation":[]},{"given":"S. Kevin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"66_CR1","unstructured":"Medical open network for artificial intelligence (monai). https:\/\/monai.io\/. Accessed 27 Feb 2022"},{"key":"66_CR2","unstructured":"Adler, J., Kohr, H., Oktem, O.: Operator discretization library (odl) (2017). Software available from https:\/\/github.com\/odlgroup\/odl"},{"issue":"2","key":"66_CR3","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato, S.G., III., 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. 38(2), 915\u2013931 (2011)","journal-title":"Med. Phys."},{"key":"66_CR4","unstructured":"Armato III, S.G., et al.: Data from lidc-idri [data set]. Cancer Imaging Arch. (2015)"},{"issue":"1","key":"66_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.202","volume":"5","author":"S Bakr","year":"2018","unstructured":"Bakr, S., et al.: A radiogenomic dataset of non-small cell lung cancer. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"66_CR6","unstructured":"Bakr, S., et al.: Data for nsclc radiogenomics collection. Cancer Imaging Arch. (2017)"},{"key":"66_CR7","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":"6","key":"66_CR8","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K.: The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Dig. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Dig. Imaging"},{"issue":"2","key":"66_CR9","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.ejrad.2009.05.054","volume":"72","author":"JT Dobbins III","year":"2009","unstructured":"Dobbins, J.T., III., McAdams, H.P.: Chest tomosynthesis: technical principles and clinical update. Eur. J. Radiol. 72(2), 244\u2013251 (2009)","journal-title":"Eur. J. Radiol."},{"issue":"2","key":"66_CR10","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1148\/radiol.12111607","volume":"264","author":"O Gevaert","year":"2012","unstructured":"Gevaert, O., et al.: Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Radiology 264(2), 387\u2013396 (2012)","journal-title":"Radiology"},{"key":"66_CR11","doi-asserted-by":"crossref","unstructured":"Han, L., Lyu, Y., Peng, C., Zhou, S.K.: Gan-based disentanglement learning for chest x-ray rib suppression. Med. Image Anal. 77, 102369 (2022)","DOI":"10.1016\/j.media.2022.102369"},{"issue":"1","key":"66_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-020-00173-2","volume":"4","author":"J Hofmanninger","year":"2020","unstructured":"Hofmanninger, J., Prayer, F., Pan, J., R\u00f6hrich, S., Prosch, H., Langs, G.: Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur. Radiol. Exp. 4(1), 1\u201313 (2020). https:\/\/doi.org\/10.1186\/s41747-020-00173-2","journal-title":"Eur. Radiol. Exp."},{"key":"66_CR13","doi-asserted-by":"crossref","unstructured":"Jin, L., et al.: Deep-learning-assisted detection and segmentation of rib fractures from ct scans: development and validation of fracnet. EBioMedicine (2020)","DOI":"10.1016\/j.ebiom.2020.103106"},{"issue":"3","key":"66_CR14","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1016\/j.crad.2011.08.017","volume":"67","author":"H Jung","year":"2012","unstructured":"Jung, H., Chung, M., Koo, J., Kim, H., Lee, K.: Digital tomosynthesis of the chest: utility for detection of lung metastasis in patients with colorectal cancer. Clin. Radiol. 67(3), 232\u2013238 (2012)","journal-title":"Clin. Radiol."},{"key":"66_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/978-3-030-12029-0_40","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"E Kerfoot","year":"2019","unstructured":"Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., Schnabel, J.A.: Left-ventricle quantification using residual u-net. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 371\u2013380. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_40"},{"key":"66_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)"},{"key":"66_CR17","doi-asserted-by":"crossref","unstructured":"Lauritsch, G., H\u00e4rer, W.H.: Theoretical framework for filtered back projection in tomosynthesis. In: Medical Imaging 1998: Image Processing, vol. 3338, pp. 1127\u20131137. International Society for Optics and Photonics (1998)","DOI":"10.1117\/12.310839"},{"issue":"10","key":"66_CR18","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1109\/TMI.2020.2986242","volume":"39","author":"H Li","year":"2020","unstructured":"Li, H., et al.: High-resolution chest x-ray bone suppression using unpaired CT structural priors. IEEE Trans. Med. Imaging 39(10), 3053\u20133063 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"66_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-030-32226-7_31","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Li","year":"2019","unstructured":"Li, Z., Li, H., Han, H., Shi, G., Wang, J., Zhou, S.K.: Encoding CT anatomy knowledge for unpaired chest X-ray image decomposition. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 275\u2013283. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_31"},{"issue":"3","key":"66_CR20","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1148\/rg.2016150184","volume":"36","author":"H Machida","year":"2016","unstructured":"Machida, H., et al.: Whole-body clinical applications of digital tomosynthesis. Radiographics 36(3), 735\u2013750 (2016)","journal-title":"Radiographics"},{"key":"66_CR21","doi-asserted-by":"crossref","unstructured":"Miroshnychenko, O., Miroshnychenko, S., Nevgasymyi, A., Khobta, Y.: Contrasts comparison of same cases of chest pathologies for radiography and tomosynthesis. In: 2020 International Symposium on Electronics and Telecommunications (ISETC), pp. 1\u20134. IEEE (2020)","DOI":"10.1109\/ISETC50328.2020.9301081"},{"issue":"2","key":"66_CR22","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.radi.2014.12.006","volume":"21","author":"N Molk","year":"2015","unstructured":"Molk, N., Seeram, E.: Digital tomosynthesis of the chest: a literature review. Radiography 21(2), 197\u2013202 (2015)","journal-title":"Radiography"},{"issue":"4","key":"66_CR23","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1177\/028418519303400407","volume":"34","author":"S Sone","year":"1993","unstructured":"Sone, S.: Chest imaging with dual-energy subtraction digital tomosynthesis. Acta Radiologica 34(4), 346\u2013350 (1993)","journal-title":"Acta Radiologica"},{"issue":"6","key":"66_CR24","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1097\/JTO.0b013e318292bdef","volume":"8","author":"A Terzi","year":"2013","unstructured":"Terzi, A., et al.: Lung cancer detection with digital chest tomosynthesis: baseline results from the observational study sos. J. Thoracic Oncol. 8(6), 685\u2013692 (2013)","journal-title":"J. Thoracic Oncol."},{"issue":"1","key":"66_CR25","doi-asserted-by":"publisher","first-page":"E204","DOI":"10.1148\/radiol.2021203957","volume":"299","author":"EB Tsai","year":"2021","unstructured":"Tsai, E.B., et al.: The RSNA international covid-19 open radiology database (ricord). Radiology 299(1), E204\u2013E213 (2021)","journal-title":"Radiology"},{"key":"66_CR26","unstructured":"Tsai, E.B., et al.: Data from the medical imaging data resource center - RSNA international covid radiology database release 1a - chest ct covid+ (midrc-ricord-1a). Data Cancer Imaging Arch. (2022)"},{"key":"66_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1007\/978-3-030-00937-3_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Unberath","year":"2018","unstructured":"Unberath, M., et al.: DeepDRR \u2013 a catalyst for machine learning in fluoroscopy-guided procedures. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 98\u2013106. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_12"},{"issue":"10","key":"66_CR28","doi-asserted-by":"publisher","first-page":"2808","DOI":"10.1109\/TMI.2021.3066161","volume":"40","author":"Q Yao","year":"2021","unstructured":"Yao, Q., Xiao, L., Liu, P., Zhou, S.K.: Label-free segmentation of Covid-19 lesions in lung CT. IEEE Trans. Med. Imaging 40(10), 2808\u20132819 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"66_CR29","doi-asserted-by":"crossref","unstructured":"Zhou, S.K., et al.: A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. In: Proceedings of the IEEE (2021)","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"66_CR30","volume-title":"Handbook of Medical Image Computing and Computer Assisted Intervention","author":"SK Zhou","year":"2019","unstructured":"Zhou, S.K., Rueckert, D., Fichtinger, G.: Handbook of Medical Image Computing and Computer Assisted Intervention. Academic Press, London (2019)"}],"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\/978-3-031-16431-6_66","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:56:04Z","timestamp":1710356164000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16431-6_66"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164309","9783031164316"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16431-6_66","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":"15 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)"}}]}}