{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:24:51Z","timestamp":1743049491701,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880095"},{"type":"electronic","value":"9783030880101"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88010-1_27","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:06:25Z","timestamp":1634857585000},"page":"325-336","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Two-Stage COVID-19 Lung Segmentation from CT Images by Integrating Rib Outlining and Contour Refinement"],"prefix":"10.1007","author":[{"given":"Qianjing","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kele","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"You-ming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"issue":"2","key":"27_CR1","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai, T., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32\u2013E40 (2020)","journal-title":"Radiology"},{"issue":"1","key":"27_CR2","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1148\/radiol.2020200230","volume":"295","author":"M Chung","year":"2020","unstructured":"Chung, M., et al.: CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1), 202\u2013207 (2020)","journal-title":"Radiology"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Deng, Y., Lei, L., Chen, Y., Zhang, W.: The potential added value of FDG PET\/CT for COVID-19 pneumonia. Eur. J. Nucl. Med. Mol. Imaging 1\u20132 (2020)","DOI":"10.1007\/s00259-020-04767-1"},{"key":"27_CR4","doi-asserted-by":"publisher","first-page":"101592","DOI":"10.1016\/j.media.2019.101592","volume":"60","author":"SE Gerard","year":"2020","unstructured":"Gerard, S.E., Herrmann, J., Kaczka, D.W., Musch, G., Fernandez-Bustamante, A., Reinhardt, J.M.: Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species. Med. Image Anal. 60, 101592 (2020)","journal-title":"Med. Image Anal."},{"key":"27_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/978-3-319-14249-4_48","volume-title":"Advances in Visual Computing","author":"G Gill","year":"2014","unstructured":"Gill, G., Beichel, R.R.: Segmentation of lungs with interstitial lung disease in CT scans: a TV-L1 based texture analysis approach. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8887, pp. 511\u2013520. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-14249-4_48"},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.media.2017.06.005","volume":"40","author":"AA Kiaei","year":"2017","unstructured":"Kiaei, A.A., Khotanlou, H.: Segmentation of medical images using mean value guided contour. Med. Image Anal. 40, 111\u2013132 (2017)","journal-title":"Med. Image Anal."},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.sigpro.2015.08.020","volume":"120","author":"Q Li","year":"2016","unstructured":"Li, Q., Deng, T., Xie, W.: Active contours driven by divergence of gradient vector flow. Sig. Process. 120, 185\u2013199 (2016)","journal-title":"Sig. Process."},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Liu, C., Pang, M.: Automatic lung segmentation based on image decomposition and wavelet transform. Biomed. Signal Process. Control 61, 102032 (2020)","DOI":"10.1016\/j.bspc.2020.102032"},{"issue":"4","key":"27_CR9","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.1148\/rg.2015140232","volume":"35","author":"A Mansoor","year":"2015","unstructured":"Mansoor, A., et al.: Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. Radiographics 35(4), 1056\u20131076 (2015)","journal-title":"Radiographics"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Mansoor, A., Bagci, U., Mollura, D.J.: Near-optimal keypoint sampling for fast pathological lung segmentation. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6032\u20136035. IEEE (2014)","DOI":"10.1109\/EMBC.2014.6945004"},{"key":"27_CR11","unstructured":"Mesanovic, N., Grgic, M., Huseinagic, H., Males, M., Skejic, E., Smajlovic, M.: Automatic CT image segmentation of the lungs with region growing algorithm. In: 18th International Conference on Systems, Signals and Image Processing-IWSSIP, pp. 395\u2013400 (2011)"},{"issue":"6","key":"27_CR12","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s10278-019-00254-8","volume":"32","author":"B Park","year":"2019","unstructured":"Park, B., Park, H., Lee, S.M., Seo, J.B., Kim, N.: Lung segmentation on HRCT and volumetric CT for diffuse interstitial lung disease using deep convolutional neural networks. J. Digit. Imaging 32(6), 1019\u20131026 (2019). https:\/\/doi.org\/10.1007\/s10278-019-00254-8","journal-title":"J. Digit. Imaging"},{"issue":"9","key":"27_CR13","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1016\/j.acra.2008.02.004","volume":"15","author":"MN Prasad","year":"2008","unstructured":"Prasad, M.N., et al.: Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs. Acad. Radiol. 15(9), 1173\u20131180 (2008)","journal-title":"Acad. Radiol."},{"issue":"4","key":"27_CR14","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1007\/s10278-016-9875-z","volume":"29","author":"AR Pulagam","year":"2016","unstructured":"Pulagam, A.R., Kande, G.B., Ede, V.K.R., Inampudi, R.B.: Automated lung segmentation from HRCT scans with diffuse parenchymal lung diseases. J. Digit. Imaging 29(4), 507\u2013519 (2016)","journal-title":"J. Digit. Imaging"},{"key":"27_CR15","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","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":"27_CR16","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.compbiomed.2014.12.008","volume":"57","author":"S Shen","year":"2015","unstructured":"Shen, S., Bui, A.A., Cong, J., Hsu, W.: An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput. Biol. Med. 57, 139\u2013149 (2015)","journal-title":"Comput. Biol. Med."},{"issue":"11","key":"27_CR17","doi-asserted-by":"publisher","first-page":"4970","DOI":"10.1002\/mp.13773","volume":"46","author":"AM Sousa","year":"2019","unstructured":"Sousa, A.M., Martins, S.B., Falcao, A.X., Reis, F., Bagatin, E., Irion, K.: ALTIS: A fast and automatic lung and trachea CT-image segmentation method. Med. Phys. 46(11), 4970\u20134982 (2019)","journal-title":"Med. Phys."},{"issue":"2","key":"27_CR18","first-page":"449","volume":"31","author":"S Sun","year":"2011","unstructured":"Sun, S., Bauer, C., Beichel, R.: Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach. IEEE Trans. Med. Imaging 31(2), 449\u2013460 (2011)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88010-1_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T08:06:30Z","timestamp":1654502790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88010-1_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880095","9783030880101"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88010-1_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"22 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/index_en.html","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":"513","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":"201","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":"39% - 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)"}},{"value":"There were 30 oral and 171 poster presentations at the conference.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}