{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:46:01Z","timestamp":1773326761117,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030718268","type":"print"},{"value":"9783030718275","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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71827-5_2","type":"book-chapter","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:02:36Z","timestamp":1615507356000},"page":"16-26","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Domain Knowledge Driven Multi-modal Segmentation of Anatomical Brain Barriers to Cancer Spread"],"prefix":"10.1007","author":[{"given":"Xiaoyang","family":"Zou","sequence":"first","affiliation":[]},{"given":"Qi","family":"Dou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,13]]},"reference":[{"key":"2_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Dou, Q., Liu, Q., Heng, P., Glocker, B.: Unpaired multi-modal segmentation via knowledge distillation. IEEE Trans. Med. Imaging 39, 2415\u20132425 (2020)","DOI":"10.1109\/TMI.2019.2963882"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Duanmu, H., et al.: Automatic brain organ segmentation with 3D fully convolutional neural network for radiation therapy treatment planning. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 758\u2013762. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098485"},{"key":"2_CR4","unstructured":"Isensee, F., J\u00e4ger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: Automated design of deep learning methods for biomedical image segmentation. arXiv preprint arXiv:1904.08128 (2019)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"2_CR6","unstructured":"Isensee, F., Petersen, J., Kohl, S.A., J\u00e4ger, P.F., Maier-Hein, K.H.: nnU-Net: breaking the spell on successful medical image segmentation, vol. 1, pp. 1\u20138. arXiv preprint arXiv:1904.08128 (2019)"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Laperriere, N., Zuraw, L., Cairncross, G., Cancer Care Ontario Practice Guidelines Initiative Neuro-Oncology Disease Site Group, et al.: Radiotherapy for newly diagnosed malignant glioma in adults: a systematic review. Radiother. Oncol. 64(3), 259\u2013273 (2002)","DOI":"10.1016\/S0167-8140(02)00078-6"},{"issue":"10","key":"2_CR8","doi-asserted-by":"publisher","first-page":"5281","DOI":"10.1002\/cam4.1757","volume":"7","author":"K Li","year":"2018","unstructured":"Li, K., et al.: Trends and patterns of incidence of diffuse glioma in adults in the united states, 1973\u20132014. Cancer Med. 7(10), 5281\u20135290 (2018)","journal-title":"Cancer Med."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Mlynarski, P., Delingette, H., Alghamdi, H., Bondiau, P.Y., Ayache, N.: Anatomically consistent segmentation of organs at risk in MRI with convolutional neural networks. arXiv preprint arXiv:1907.02003 (2019)","DOI":"10.1117\/1.JMI.7.1.014502"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ostrom, Q.T., et al.: CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the united states in 2012\u20132016. Neuro-Oncol. 21(Suppl.$$\\_$$5), v1\u2013v100 (2019)","DOI":"10.1093\/neuonc\/noz150"},{"key":"2_CR11","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, pp. 8026\u20138037 (2019)"},{"key":"2_CR12","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":"2_CR13","doi-asserted-by":"crossref","unstructured":"Rumboldt, Z., Castillo, M., Huang, B., Rossi, A.: Brain Imaging with MRI and CT: An Image Pattern Approach. Cambridge University Press, Cambridge (2012)","DOI":"10.1017\/CBO9781139030854"},{"key":"2_CR14","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.radonc.2020.01.028","volume":"146","author":"N Shusharina","year":"2020","unstructured":"Shusharina, N., S\u00f6derberg, J., Edmunds, D., L\u00f6fman, F., Shih, H., Bortfeld, T.: Automated delineation of the clinical target volume using anatomically constrained 3D expansion of the gross tumor volume. Radiother. Oncol. 146, 37\u201343 (2020)","journal-title":"Radiother. Oncol."},{"issue":"1","key":"2_CR15","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s00066-003-0976-5","volume":"179","author":"E Weiss","year":"2003","unstructured":"Weiss, E., Hess, C.F.: The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy. Strahlentherapie und Onkologie 179(1), 21\u201330 (2003)","journal-title":"Strahlentherapie und Onkologie"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Yushkevich, P.A., Gao, Y., Gerig, G.: ITK-SNAP: an interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3342\u20133345. IEEE (2016)","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"2_CR17","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214\u2013224 (2015)","journal-title":"NeuroImage"}],"container-title":["Lecture Notes in Computer Science","Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71827-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:03:47Z","timestamp":1773277427000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71827-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030718268","9783030718275"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71827-5_2","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":"13 March 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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}