{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T05:55:50Z","timestamp":1768802150160,"version":"3.49.0"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030871925","type":"print"},{"value":"9783030871932","type":"electronic"}],"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\/978-3-030-87193-2_54","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T20:25:10Z","timestamp":1632342310000},"page":"569-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images"],"prefix":"10.1007","author":[{"given":"Zijie","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjun","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diping","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixu","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Brouwer, C.L., Steenbakkers, R.J.H.M., Heuvel, E.V.d., et al.: 3D variation in delineation of head and neck organs at risk. Radiat. Oncol. 7(1), 32 (2012)","DOI":"10.1186\/1748-717X-7-32"},{"issue":"1","key":"54_CR2","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1088\/0031-9155\/57\/1\/93","volume":"57","author":"A Chen","year":"2012","unstructured":"Chen, A., Niermann, K.J., Deeley, M.A., Dawant, B.M.: Evaluation of multiple-atlas-based strategies for segmentation of the thyroid gland in head and neck CT images for IMRT. Phys. Med. Biol. 57(1), 93\u2013111 (2012)","journal-title":"Phys. Med. Biol."},{"issue":"6","key":"54_CR3","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K., Vendt, B., Smith, K., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"key":"54_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/978-3-030-32248-9_92","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"Y Gao","year":"2019","unstructured":"Gao, Y., et al.: FocusNet: imbalanced large and small organ segmentation with an end-to-end deep neural network for head and neck CT images. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 829\u2013838. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_92"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Guo, D., et al.: Organ at risk segmentation for head and neck cancer using stratified learning and neural architecture search. In: 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4223\u20134232. Virtual Conference (2020)","DOI":"10.1109\/CVPR42600.2020.00428"},{"key":"54_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1007\/978-3-540-85990-1_52","volume-title":"Medical Image Computing and Computer-Assisted Intervention","author":"X Han","year":"2008","unstructured":"Han, X., et al.: Atlas-based auto-segmentation of head and neck CT images. In: Metaxas, D., Axel, L., Fichtinger, G., Sz\u00e9kely, G. (eds.) MICCAI 2008. LNCS, vol. 5242, pp. 434\u2013441. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-85990-1_52"},{"issue":"3","key":"54_CR7","doi-asserted-by":"publisher","first-page":"950","DOI":"10.1016\/j.ijrobp.2009.09.062","volume":"77","author":"PM Harari","year":"2010","unstructured":"Harari, P.M., Song, S., Tome, W.A.: Emphasizing conformal avoidance versus target definition for IMRT planning in head-and-neck cancer. Int. J. Radiat. Oncol. Biol. Phys. 77(3), 950\u2013958 (2010)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"2","key":"54_CR8","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1002\/mp.12045","volume":"44","author":"B Ibragimov","year":"2017","unstructured":"Ibragimov, B., Xing, L.: Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks. Med. Phys. 44(2), 547\u2013557 (2017)","journal-title":"Med. Phys."},{"key":"54_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-030-32245-8_7","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"C Li","year":"2019","unstructured":"Li, C., Sun, H., Liu, Z., Wang, M., Zheng, H., Wang, S.: Learning cross-modal deep representations for multi-modal MR image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 57\u201365. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_7"},{"issue":"9","key":"54_CR10","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.1109\/TMI.2020.2975853","volume":"39","author":"S Liang","year":"2020","unstructured":"Liang, S., Thung, K.-H., Nie, D., Zhang, Y., Shen, D.: Multi-view spatial aggregation framework for joint localization and segmentation of organs at risk in head and neck CT images. IEEE Trans. Med. Imaging 39(9), 2794\u20132805 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"54_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/978-3-030-00934-2_94","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"S Liu","year":"2018","unstructured":"Liu, S., et al.: 3D anisotropic hybrid network: transferring convolutional features from 2D images to 3D anisotropic volumes. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 851\u2013858. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00934-2_94"},{"issue":"1","key":"54_CR12","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/mp.13296","volume":"46","author":"K Men","year":"2019","unstructured":"Men, K., Geng, H., Cheng, C., et al.: More accurate and efficient segmentation of organs-at-risk in radiotherapy with convolutional neural networks cascades. Med. Phys. 46(1), 286\u2013292 (2019)","journal-title":"Med. Phys."},{"key":"54_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-32248-9_28","volume-title":"Medical Image Computing and Computer Assisted Intervention","author":"K Qi","year":"2019","unstructured":"Qi, K., et al.: X-Net: brain stroke lesion segmentation based on depthwise separable convolution and long-range dependencies. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 247\u2013255. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_28"},{"issue":"5","key":"54_CR14","doi-asserted-by":"publisher","first-page":"2020","DOI":"10.1002\/mp.12197","volume":"44","author":"PF Raudaschl","year":"2017","unstructured":"Raudaschl, P.F., Zaffino, P., Sharp, G.C., et al.: Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015. Med. Phys. 44(5), 2020\u20132036 (2017)","journal-title":"Med. Phys."},{"issue":"5","key":"54_CR15","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1002\/mp.12837","volume":"45","author":"X Ren","year":"2018","unstructured":"Ren, X., et al.: Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images. Med. Phys. 45(5), 2063\u20132075 (2018)","journal-title":"Med. Phys."},{"key":"54_CR16","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"},{"issue":"10","key":"54_CR17","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1038\/s42256-019-0099-z","volume":"1","author":"H Tang","year":"2019","unstructured":"Tang, H., Chen, X., Liu, Y., et al.: Clinically applicable deep learning framework for organs at risk delineation in CT images. Nat. Mach. Intell. 1(10), 480\u2013491 (2019)","journal-title":"Nat. Mach. Intell."},{"key":"54_CR18","doi-asserted-by":"crossref","unstructured":"Tang, H., Liu, X., Han, K., et al.: Spatial context-aware self-attention model for multi-organ segmentation. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 939\u2013949. Virtual Conference (2021)","DOI":"10.1109\/WACV48630.2021.00098"},{"issue":"2","key":"54_CR19","first-page":"87","volume":"65","author":"LA Torre","year":"2015","unstructured":"Torre, L.A., Bray, F., Siegel, R.L., Ferlay, J., Lortet-Tieulent, J., Jemal, A.: Global cancer statistics, 2012. Ophthalmology 65(2), 87\u2013108 (2015)","journal-title":"Ophthalmology"},{"key":"54_CR20","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., et al.: Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1451\u20131460. Lake Tahoe, NV, USA (2018)","DOI":"10.1109\/WACV.2018.00163"},{"issue":"2","key":"54_CR21","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/TIP.2017.2768621","volume":"27","author":"Z Wang","year":"2018","unstructured":"Wang, Z., Wei, L., Wang, L., Gao, Y., Chen, W., Shen, D.: Hierarchical vertex regression-based segmentation of head and neck CT images for radiotherapy planning. IEEE Trans. Image. Process. 27(2), 923\u2013937 (2018)","journal-title":"IEEE Trans. Image. Process."},{"issue":"2","key":"54_CR22","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1002\/mp.13300","volume":"46","author":"W Zhu","year":"2019","unstructured":"Zhu, W., et al.: AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med. Phys. 46(2), 576\u2013589 (2019)","journal-title":"Med. Phys."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87193-2_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T06:22:05Z","timestamp":1699510925000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87193-2_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871925","9783030871932"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87193-2_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","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":"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":"1622","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":"531","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":"33% - 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":"4","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":"The conference was held virtually.","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)"}}]}}