{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:28:15Z","timestamp":1743060495332,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030134686"},{"type":"electronic","value":"9783030134693"}],"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\/978-3-030-13469-3_90","type":"book-chapter","created":{"date-parts":[[2019,3,2]],"date-time":"2019-03-02T13:03:53Z","timestamp":1551531833000},"page":"774-782","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced Graph Cuts for Brain Tumor Segmentation Using Bayesian Optimization"],"prefix":"10.1007","author":[{"given":"Mauricio","family":"Casta\u00f1o","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hern\u00e1n F.","family":"Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gloria L.","family":"Porras-Hurtado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro A.","family":"Orozco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jorge I.","family":"Marin-Hurtado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,3]]},"reference":[{"key":"90_CR1","unstructured":"Beers, A., et al.: Sequential 3D U-Nets for biologically-informed brain tumor segmentation. CoRR abs\/1709.02967 (2017)"},{"key":"90_CR2","doi-asserted-by":"crossref","unstructured":"Bharath, H.N., Colleman, S., Sima, D.M., Van Huffel, S.: Tumor segmentation from multi-parametric MRI using random forest with superpixel and tensor based feature extraction. In: Brats Challenge 2017 (2017)","DOI":"10.1007\/978-3-319-75238-9_39"},{"key":"90_CR3","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","volume":"70","author":"Y Boykov","year":"2006","unstructured":"Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70, 109\u2013131 (2006)","journal-title":"Int. J. Comput. Vis."},{"key":"90_CR4","unstructured":"Castillo, L.S., Daza, L.A., Rivera, L.C., Arbel\u00e1ez, P.: Volumetric multimodality neural network for brain tumor segmentation (2017)"},{"issue":"1","key":"90_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11263-011-0437-z","volume":"96","author":"A Delong","year":"2012","unstructured":"Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. Int. J. Comput. Vis. 96(1), 1\u201327 (2012)","journal-title":"Int. J. Comput. Vis."},{"key":"90_CR6","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1016\/j.procs.2018.05.089","volume":"132","author":"J Dogra","year":"2018","unstructured":"Dogra, J., Jain, S., Sood, M.: Segmentation of MR images using hybrid kmean-graph cut technique. Proc. Comput. Sci. 132, 775\u2013784 (2018). International Conference on Computational Intelligence and Data Science","journal-title":"Proc. Comput. Sci."},{"key":"90_CR7","unstructured":"Gonzalez, J., Longworth, J., James, D., Lawrence, N.: Bayesian optimisation for synthetic gene design. In: NIPS Workshop on Bayesian Optimization in Academia and Industry (2014)"},{"key":"90_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with deep neural networks. Med. Image Anal. 35, 18\u201331 (2017)","journal-title":"Med. Image Anal."},{"key":"90_CR9","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.procs.2016.09.407","volume":"102","author":"A Isin","year":"2016","unstructured":"Isin, A., Direkoglu, C., Sah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Proc. Comput. Sci. 102, 317\u2013324 (2016). 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29\u201330 August 2016, Vienna, Austria","journal-title":"Proc. Comput. Sci."},{"key":"90_CR10","unstructured":"Ji\u0159\u00edk, M., Lukes, V., Svobodova, M., \u017delezn\u00fd, M.: Image segmentation in medical imaging via graph-cuts (2013)"},{"issue":"4","key":"90_CR11","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1023\/A:1012771025575","volume":"21","author":"DR Jones","year":"2001","unstructured":"Jones, D.R.: A taxonomy of global optimization methods based on response surfaces. J. Glob. Optim. 21(4), 345\u2013383 (2001)","journal-title":"J. Glob. Optim."},{"issue":"10","key":"90_CR12","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"90_CR13","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press (2005)","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"90_CR14","unstructured":"Shengcong Chen, C.D., Zhou, C.: Brain tumor segmentation with label distribution learning and multi-level feature representation. In: Brats Challenge 2017 (2017)"},{"key":"90_CR15","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2951\u20132959. Curran Associates, Inc. (2012)"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-13469-3_90","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T01:16:24Z","timestamp":1677719784000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-13469-3_90"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030134686","9783030134693"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-13469-3_90","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"3 March 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","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":"19 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2018","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":"ciarp2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/atvs.ii.uam.es\/ciarp2018\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"187","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":"112","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":"60% - 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":"2,94","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":"5","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"}]}}