{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:56:32Z","timestamp":1765356992179,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872397"},{"type":"electronic","value":"9783030872403"}],"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\/978-3-030-87240-3_49","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T07:44:03Z","timestamp":1632383043000},"page":"510-518","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["3D Brain Midline Delineation for Hematoma Patients"],"prefix":"10.1007","author":[{"given":"Chenchen","family":"Qin","sequence":"first","affiliation":[]},{"given":"Haoming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yixun","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Hanqi","family":"Pei","sequence":"additional","affiliation":[]},{"given":"Xiaoning","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yihao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jianbo","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Renzhi","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Yao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"3","key":"49_CR1","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1016\/j.emc.2012.06.003","volume":"30","author":"JA Caceres","year":"2012","unstructured":"Caceres, J.A., Goldstein, J.N.: Intracranial hemorrhage. Emerg. Med. Clin. North Am. 30(3), 771 (2012)","journal-title":"Emerg. Med. Clin. North Am."},{"key":"49_CR2","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"49_CR3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"3","key":"49_CR4","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.compbiomed.2010.01.004","volume":"40","author":"CC Liao","year":"2010","unstructured":"Liao, C.C., Xiao, F., Wong, J.M., Chiang, I.J.: Automatic recognition of midline shift on brain CT images. Comput. Biol. Med. 40(3), 331\u2013339 (2010)","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"49_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2013.11.001","volume":"38","author":"R Liu","year":"2014","unstructured":"Liu, R., et al.: Automatic detection and quantification of brain midline shift using anatomical marker model. Comput. Med. Imaging Graph. 38(1), 1\u201314 (2014)","journal-title":"Comput. Med. Imaging Graph."},{"key":"49_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/978-3-030-33850-3_4","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support","author":"M Pisov","year":"2019","unstructured":"Pisov, M., et al.: Incorporating task-specific structural knowledge into CNNs for brain midline shift detection. In: Suzuki, K., et al. (eds.) ML-CDS\/IMIMIC -2019. LNCS, vol. 11797, pp. 30\u201338. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33850-3_4"},{"key":"49_CR7","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 \u2013 MICCAI 2015","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":"49_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","volume":"365","author":"L Rundo","year":"2019","unstructured":"Rundo, L., et al.: USE-NET: incorporating squeeze-and-excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets. Neurocomputing 365, 31\u201343 (2019)","journal-title":"Neurocomputing"},{"key":"49_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/978-3-030-59728-3_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Wang","year":"2020","unstructured":"Wang, S., Liang, K., Li, Y., Yu, Y., Wang, Y.: Context-aware refinement network incorporating structural connectivity prior for brain midline delineation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 208\u2013217. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_21"},{"key":"49_CR10","doi-asserted-by":"crossref","unstructured":"Wang, W.Z., et al.: Minimally invasive craniopuncture therapy vs. conservative treatment for spontaneous intracerebral hemorrhage: results from a randomized clinical trial in china. Int. J. Stroke 4(1), 11\u201316 (2009)","DOI":"10.1111\/j.1747-4949.2009.00239.x"},{"key":"49_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/978-3-030-32248-9_93","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Wei","year":"2019","unstructured":"Wei, H., et al.: Regression-based line detection network for delineation of largely deformed brain midline. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 839\u2013847. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_93"}],"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\/978-3-030-87240-3_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T23:07:34Z","timestamp":1638659254000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87240-3_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872397","9783030872403"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87240-3_49","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":"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)"}}]}}