{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T23:11:09Z","timestamp":1776813069566,"version":"3.51.2"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819964888","type":"print"},{"value":"9789819964895","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\/978-981-99-6489-5_11","type":"book-chapter","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T20:30:58Z","timestamp":1696969858000},"page":"132-144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Modified BiSeNet for Spinal Segmentation"],"prefix":"10.1007","author":[{"given":"Yunjiao","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daxing","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulei","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"issue":"3","key":"11_CR1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s42979-021-00618-4","volume":"2","author":"S Garg","year":"2021","unstructured":"Garg, S., Bhagyashree, S.R.: Spinal cord MRI segmentation techniques and algorithms: a survey. SN Comput. Sci. 2(3), 229 (2021)","journal-title":"SN Comput. Sci."},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.jneumeth.2018.07.015","volume":"308","author":"M Fouladivanda","year":"2018","unstructured":"Fouladivanda, M., Kazemi, K., Helfroush, M.S., et al.: Morphological active contour driven by local and global intensity fitting for spinal cord segmentation from MR images. J. Neurosci. Methods 308, 116\u2013128 (2018)","journal-title":"J. Neurosci. Methods"},{"key":"11_CR3","first-page":"164","volume":"340","author":"A Eltanboly","year":"2019","unstructured":"Eltanboly, A., Ghazal, M., Hajjdiab, H., et al.: Level sets-based image segmentation approach using statistical shape priors. Appl. Math. Comput. 340, 164\u2013179 (2019)","journal-title":"Appl. Math. Comput."},{"key":"11_CR4","doi-asserted-by":"publisher","unstructured":"Yu, W., Liu, W., Tan, L., Zhang, S., Zheng, G.: Multi-object model-based multi-atlas segmentation constrained grid cut for automatic segmentation of lumbar vertebrae from CT images. In: Zheng, G., Tian, W., Zhuang, X. (eds.) Intelligent Orthopaedics. AEMB, vol. 1093, pp. 65\u201371. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-13-1396-7_5","DOI":"10.1007\/978-981-13-1396-7_5"},{"key":"11_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2017.12.013","volume":"155","author":"G Hille","year":"2018","unstructured":"Hille, G., Saalfeld, S., Serowy, S., et al.: Vertebral body segmentation in wide range clinical routine spine MRI data. Comput. Methods Programs Biomed. 155, 93\u201399 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"11_CR6","doi-asserted-by":"publisher","first-page":"99","DOI":"10.4103\/jcvjs.JCVJS_37_20","volume":"11","author":"K Siemionow","year":"2020","unstructured":"Siemionow, K., Luciano, C., Forsthoefel, C., et al.: Autonomous image segmentation and identification of anatomical landmarks from lumbar spine intraoperative computed tomography scans using machine learning: a validation study. J. Craniovertebral Junction Spine 11(2), 99 (2020)","journal-title":"J. Craniovertebral Junction Spine"},{"issue":"4","key":"11_CR7","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.3390\/s22041547","volume":"22","author":"M Mushtaq","year":"2022","unstructured":"Mushtaq, M., Akram, M.U., Alghamdi, N.S., et al.: Localization and edge-based segmentation of lumbar spine vertebrae to identify the deformities using deep learning models. Sensors 22(4), 1547 (2022)","journal-title":"Sensors"},{"key":"11_CR8","doi-asserted-by":"publisher","unstructured":"Chen, H., Dou, Q., Wang, X., Qin, J., Cheng, J.C.Y., Heng, PA.: 3D fully convolutional networks for intervertebral disc localization and segmentation. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, SL. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 375\u2013382. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-43775-0_34","DOI":"10.1007\/978-3-319-43775-0_34"},{"key":"11_CR9","doi-asserted-by":"publisher","unstructured":"Hutt, H., Everson, R., Meakin, J.: 3D Intervertebral Disc segmentation from MRI using supervoxel-based CRFs. In: Vrtovec, T.,\u00a0et al. (eds.)\u00a0Computational Methods and Clinical Applications for Spine Imaging. CSI 2015. LNCS, vol. 9402, pp. 125\u2013129. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-41827-8_12","DOI":"10.1007\/978-3-319-41827-8_12"},{"issue":"7","key":"11_CR10","doi-asserted-by":"publisher","first-page":"2051","DOI":"10.1109\/TBME.2011.2135351","volume":"58","author":"J Lee","year":"2011","unstructured":"Lee, J., Kim, S., Kim, Y.S., et al.: Automated segmentation of the lumbar pedicle in CT images for spinal fusion surgery. IEEE Trans. Biomed. Eng. 58(7), 2051 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR11","first-page":"1","volume":"2021","author":"Y Wei","year":"2021","unstructured":"Wei, Y., Wang, X.: An improved image segmentation algorithm CT superpixel grid using active contour. Wirel. Commun. Mob. Comput. 2021, 1\u20139 (2021)","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s10278-019-00216-0","volume":"33","author":"F Rehman","year":"2020","unstructured":"Rehman, F., Ali Shah, S.I., Riaz, M.N., et al.: A region-based deep level set formulation for vertebral bone segmentation of osteoporotic fractures. J. Digit. Imaging 33, 191\u2013203 (2020)","journal-title":"J. Digit. Imaging"},{"key":"11_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102683","volume":"83","author":"L Xie","year":"2023","unstructured":"Xie, L., Wisse, L.E.M., Wang, J., et al.: Deep label fusion: a generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation. Med. Image Anal. 83, 102683 (2023)","journal-title":"Med. Image Anal."},{"issue":"1","key":"11_CR14","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.1.015002","volume":"7","author":"R Haq","year":"2020","unstructured":"Haq, R., Schmid, J., Borgie, R., et al.: Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation. J. Med. Imaging 7(1), 015002 (2020)","journal-title":"J. Med. Imaging"},{"issue":"2","key":"11_CR15","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3390\/informatics8020040","volume":"8","author":"N Altini","year":"2021","unstructured":"Altini, N., De Giosa, G., Fragasso, N., et al.: Segmentation and identification of vertebrae in CT scans using CNN, k-means clustering and k-NN. Informatics 8(2), 40 (2021)","journal-title":"Informatics"},{"key":"11_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijleo.2022.170277","volume":"272","author":"B Wang","year":"2023","unstructured":"Wang, B., Qin, J., Lv, L., et al.: MLKCA-Unet: multiscale large-kernel convolution and attention in Unet for spine MRI segmentation. Optik 272, 170277 (2023)","journal-title":"Optik"},{"issue":"9","key":"11_CR17","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.3390\/app8091656","volume":"8","author":"S Kim","year":"2018","unstructured":"Kim, S., Bae, W.C., Masuda, K., et al.: Fine-grain segmentation of the intervertebral discs from MR spine images using deep convolutional neural networks: BSU-Net. Appl. Sci. 8(9), 1656 (2018)","journal-title":"Appl. Sci."},{"key":"11_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2022.100055","volume":"2","author":"S Guinebert","year":"2022","unstructured":"Guinebert, S., Petit, E., Bousson, V., et al.: Automatic semantic segmentation and detection of vertebras and intervertebral discs by neural networks. Comput. Methods Programs Biomed. Update 2, 100055 (2022)","journal-title":"Comput. Methods Programs Biomed. Update"},{"issue":"4","key":"11_CR19","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1109\/JBHI.2018.2872810","volume":"23","author":"F Fallah","year":"2018","unstructured":"Fallah, F., Walter, S.S., Bamberg, F., et al.: Simultaneous volumetric segmentation of vertebral bodies and intervertebral discs on fat-water MR images. IEEE J. Biomed. Health Inform. 23(4), 1692\u20131701 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"11_CR20","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.spinee.2019.11.010","volume":"20","author":"J Huang","year":"2020","unstructured":"Huang, J., Shen, H., Wu, J., et al.: Spine explorer: a deep learning based fully automated program for efficient and reliable quantifications of the vertebrae and discs on sagittal lumbar spine MR images. Spine J. 20(4), 590\u2013599 (2020)","journal-title":"Spine J."},{"key":"11_CR21","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.media.2018.08.005","volume":"50","author":"Z Han","year":"2018","unstructured":"Han, Z., Wei, B., Mercado, A., et al.: Spine-GAN: semantic segmentation of multiple spinal structures. Med. Image Anal. 50, 23\u201335 (2018)","journal-title":"Med. Image Anal."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Neubert, A., Fripp, J., Shen, K., et al.: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI. In: 2011 International Conference on Digital Image Computing: Techniques and Applications, pp. 19\u201324. IEEE (2011)","DOI":"10.1109\/DICTA.2011.12"},{"issue":"9","key":"11_CR23","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TBME.2013.2256460","volume":"60","author":"AB Oktay","year":"2013","unstructured":"Oktay, A.B., Akgul, Y.S.: Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM-based MRF. IEEE Trans. Biomed. Eng. 60(9), 2375\u20132383 (2013)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR24","unstructured":"Lu J.T., Pedemonte S., Bizzo B., et al.: Deep spine: automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. In: Finale, D., Jim, F., Ken, J., et al. (eds.) Machine Learning for Healthcare Conference, pp. 403\u2013419. PMLR (2018)"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"15972","DOI":"10.1016\/j.bone.2021.115972","volume":"149","author":"A Suri","year":"2021","unstructured":"Suri, A., Jones, B.C., Ng, G., et al.: A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs. Bone 149, 15972 (2021)","journal-title":"Bone"},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1007\/s11548-018-1818-3","volume":"13","author":"M Wimmer","year":"2018","unstructured":"Wimmer, M., Major, D., Novikov, A.A., et al.: Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images. Int. J. Comput. Assist. Radiol. Surg. 13, 1591\u20131603 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"11_CR27","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1109\/TMI.2020.3025087","volume":"40","author":"S Pang","year":"2020","unstructured":"Pang, S., Pang, C., Zhao, L., et al.: SpineParseNet: spine parsing for volumetric MR image by a two-stage segmentation framework with semantic image representation. IEEE Trans. Med. Imaging 40(1), 262\u2013273 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, T., Chen, Z., et al.: SPA-ResUNet: strip pooling attention resunet for multi-class segmentation of vertebrae and intervertebral discs. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, pp.1\u20135 (2022)","DOI":"10.1109\/ISBI52829.2022.9761577"},{"key":"11_CR29","doi-asserted-by":"publisher","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., Sang, N.: BiSeNet: bilateral segmentation network for real-time semantic segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 334\u2013349. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01261-8_20","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"11_CR30","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"11_CR31","doi-asserted-by":"publisher","first-page":"368","DOI":"10.3390\/bioengineering9080368","volume":"9","author":"H Rahman","year":"2022","unstructured":"Rahman, H., Bukht, T.F.N., Imran, A., et al.: A deep learning approach for liver and tumor segmentation in CT images using ResUNet. Bioengineering 9(8), 368 (2022)","journal-title":"Bioengineering"}],"container-title":["Lecture Notes in Computer Science","Intelligent Robotics and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6489-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T20:31:58Z","timestamp":1696969918000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6489-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819964888","9789819964895"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6489-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"11 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Robotics and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icira2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icira2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"630","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":"431","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":"68% - 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","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":"2","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)"}}]}}