{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:56:54Z","timestamp":1773248214731,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030117252","type":"print"},{"value":"9783030117269","type":"electronic"}],"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\/978-3-030-11726-9_18","type":"book-chapter","created":{"date-parts":[[2019,1,25]],"date-time":"2019-01-25T13:24:06Z","timestamp":1548422646000},"page":"199-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Segmentation of Gliomas and Prediction of Patient Overall Survival: A Simple and Fast Procedure"],"prefix":"10.1007","author":[{"given":"Elodie","family":"Puybareau","sequence":"first","affiliation":[]},{"given":"Guillaume","family":"Tochon","sequence":"additional","affiliation":[]},{"given":"Joseph","family":"Chazalon","sequence":"additional","affiliation":[]},{"given":"Jonathan","family":"Fabrizio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,26]]},"reference":[{"issue":"4","key":"18_CR1","doi-asserted-by":"publisher","first-page":"262","DOI":"10.2174\/157340507782446241","volume":"3","author":"ED Angelini","year":"2007","unstructured":"Angelini, E.D., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma dynamics and computational models: a review of segmentation, registration, and in silico growth algorithms and their clinical applications. Curr. Med. Imaging Rev. 3(4), 262\u2013276 (2007)","journal-title":"Curr. Med. Imaging Rev."},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q","DOI":"10.7937\/K9\/TCIA.2017.KLXWJJ1Q"},{"key":"18_CR3","doi-asserted-by":"publisher","unstructured":"Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017). https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF","DOI":"10.7937\/K9\/TCIA.2017.GJQ7R0EF"},{"key":"18_CR4","doi-asserted-by":"publisher","first-page":"170117","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017)","journal-title":"Sci. Data"},{"key":"18_CR5","unstructured":"Bakas, S., Reyes, M., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018)"},{"issue":"13","key":"18_CR6","doi-asserted-by":"publisher","first-page":"R97","DOI":"10.1088\/0031-9155\/58\/13\/R97","volume":"58","author":"S Bauer","year":"2013","unstructured":"Bauer, S., Wiest, R., Nolte, L.P., Reyes, M.: A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol. 58(13), R97 (2013)","journal-title":"Phys. Med. Biol."},{"key":"18_CR7","unstructured":"Bonn\u00edn Rossell\u00f3, C.: Brain lesion segmentation using Convolutional Neuronal Networks. B.S. thesis, Universitat Polit\u00e8cnica de Catalunya (2018)"},{"issue":"2","key":"18_CR8","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.ejca.2008.10.026","volume":"45","author":"EA Eisenhauer","year":"2009","unstructured":"Eisenhauer, E.A., et al.: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45(2), 228\u2013247 (2009)","journal-title":"Eur. J. Cancer"},{"issue":"6","key":"18_CR9","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1097\/00019052-200112000-00002","volume":"14","author":"EC Holland","year":"2001","unstructured":"Holland, E.C.: Progenitor cells and glioma formation. Curr. Opin. Neurol. 14(6), 683\u2013688 (2001)","journal-title":"Curr. Opin. Neurol."},{"key":"18_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs\/1412.6980 (2014)"},{"key":"18_CR11","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"2","key":"18_CR13","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1007\/s00401-007-0243-4","volume":"114","author":"DN Louis","year":"2007","unstructured":"Louis, D.N., et al.: The 2007 who classification of tumours of the central nervous system. Acta Neuropathol. 114(2), 97\u2013109 (2007)","journal-title":"Acta Neuropathol."},{"key":"18_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-319-46723-8_17","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"K-K Maninis","year":"2016","unstructured":"Maninis, K.-K., Pont-Tuset, J., Arbel\u00e1ez, P., Van Gool, L.: Deep retinal image understanding. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 140\u2013148. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_17"},{"issue":"10","key":"18_CR15","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"18_CR16","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1093\/jnen\/64.6.479","volume":"64","author":"H Ohgaki","year":"2005","unstructured":"Ohgaki, H., Kleihues, P.: Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. J. Neuropathol. Exp. Neurol. 64(6), 479\u2013489 (2005)","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"18_CR17","unstructured":"Reddi, S.J., Kale, S., Kumar, S.: On the convergence of Adam and beyond. In: International Conference on Learning Representations (2018)"},{"key":"18_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"issue":"1","key":"18_CR19","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/1471-2105-8-25","volume":"8","author":"C Strobl","year":"2007","unstructured":"Strobl, C., Boulesteix, A.L., Zeileis, A., Hothorn, T.: Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 8(1), 25 (2007)","journal-title":"BMC Bioinform."},{"issue":"6","key":"18_CR20","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik, V., Liaw, A., Tong, C., Culberson, J.C., Sheridan, R.P., Feuston, B.P.: Random forest: a classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 43(6), 1947\u20131958 (2003)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"11","key":"18_CR21","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1200\/JCO.2009.26.3541","volume":"28","author":"PY Wen","year":"2010","unstructured":"Wen, P.Y., et al.: Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28(11), 1963\u20131972 (2010)","journal-title":"J. Clin. Oncol."},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Xu, Y., G\u00e9raud, T., Bloch, I.: From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning. In: Proceedings of the 23rd IEEE International Conference on Image Processing (ICIP), Beijing, China, pp. 4417\u20134421, September 2017","DOI":"10.1109\/ICIP.2017.8297117"},{"issue":"6","key":"18_CR23","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1109\/TPAMI.2015.2441070","volume":"38","author":"Y Xu","year":"2016","unstructured":"Xu, Y., G\u00e9raud, T., Najman, L.: Connected filtering on tree-based shape-spaces. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1126\u20131140 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"18_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-319-75238-9_42","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Y Xu","year":"2018","unstructured":"Xu, Y., G\u00e9raud, T., Puybareau, \u00c9., Bloch, I., Chazalon, J.: White matter hyperintensities segmentation in a few seconds using fully convolutional network and transfer learning. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 501\u2013514. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75238-9_42"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11726-9_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:43:47Z","timestamp":1710333827000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11726-9_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030117252","9783030117269"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11726-9_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Granada","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.brainlesion-workshop.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":"95","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":"92","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":"97% - 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":"3","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"}]}}