{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:15:01Z","timestamp":1743041701294,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322502"},{"type":"electronic","value":"9783030322519"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32251-9_35","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"316-325","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Vertebrae Recognition from Arbitrary Spine MRI Images by a Hierarchical Self-calibration Detection Framework"],"prefix":"10.1007","author":[{"given":"Shen","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"5","key":"35_CR1","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.1109\/TMI.2018.2798293","volume":"37","author":"H Liao","year":"2018","unstructured":"Liao, H., Mesfin, A., Luo, J.: Joint vertebrae identification and localization in spinal CT images by combining short-and long-range contextual information. IEEE Trans. Med. Imaging 37(5), 1266\u20131275 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"35_CR2","doi-asserted-by":"publisher","unstructured":"Philip, F.: Patient Safety in Surgery. Springer, London (2014). https:\/\/doi.org\/10.1007\/978-1-4471-4369-7","DOI":"10.1007\/978-1-4471-4369-7"},{"key":"35_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/978-3-319-59050-9_50","volume-title":"Information Processing in Medical Imaging","author":"D Yang","year":"2017","unstructured":"Yang, D., et al.: Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 633\u2013644. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_50"},{"key":"35_CR4","series-title":"Lecture Notes in Computational Vision and Biomechanics","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/978-3-319-07269-2_19","volume-title":"Computational Methods and Clinical Applications for Spine Imaging","author":"M Lootus","year":"2014","unstructured":"Lootus, M., Kadir, T., Zisserman, A.: Vertebrae detection and labelling in lumbar MR images. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 17, pp. 219\u2013230. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07269-2_19"},{"key":"35_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/978-3-319-24553-9_63","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"H Chen","year":"2015","unstructured":"Chen, H., et al.: Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 515\u2013522. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_63"},{"key":"35_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-642-40763-5_33","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"B Glocker","year":"2013","unstructured":"Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 262\u2013270. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_33"},{"issue":"5","key":"35_CR7","doi-asserted-by":"publisher","first-page":"1571","DOI":"10.1109\/JBHI.2017.2776246","volume":"22","author":"S Zhao","year":"2018","unstructured":"Zhao, S., et al.: Robust segmentation of intima-media borders with different morphologies and dynamics during the cardiac cycle. IEEE J. Biomed. Health 22(5), 1571\u20131582 (2018)","journal-title":"IEEE J. Biomed. Health"},{"key":"35_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, R., Liao, W., Zou, B., Chen, Z., Li, S.: Weakly-supervised simultaneous evidence identification and segmentation for automated glaucoma diagnosis. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.3301809"},{"key":"35_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.01.004","volume":"37","author":"Z Gao","year":"2017","unstructured":"Gao, Z., et al.: Robust estimation of carotid artery wall motion using the elasticity-based state-space approach. Med. Image Anal. 37, 1\u201321 (2017)","journal-title":"Med. Image Anal."},{"key":"35_CR10","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 1\u201314 (2015)"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Lin, T., Dollar, P., Girshick, R., He, K., Hariharan, B., Belon, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"35_CR13","first-page":"236","volume":"8","author":"J Yedidia","year":"2003","unstructured":"Yedidia, J., Freeman, W., Weiss, Y.: Understanding belief propagation and its generalizations. Exploring Artif. Intell. New Millennium 8, 236\u2013239 (2003)","journal-title":"Exploring Artif. Intell. New Millennium"},{"issue":"2","key":"35_CR14","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van, G., Williams, C., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vis."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32251-9_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:17:41Z","timestamp":1728519461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"3.07","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":"6.31","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"}]}}