{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T21:21:54Z","timestamp":1757452914963,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322472"},{"type":"electronic","value":"9783030322489"}],"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\/978-3-030-32248-9_95","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"856-863","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Infarct Segmentation from Follow-up Non-Contrast CT Scans in Patients with Acute Ischemic Stroke Using Dense Multi-Path Contextual Generative Adversarial Network"],"prefix":"10.1007","author":[{"given":"Hulin","family":"Kuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bijoy K.","family":"Menon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wu","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"11","key":"95_CR1","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1056\/NEJMoa1414905","volume":"372","author":"M Goyal","year":"2015","unstructured":"Goyal, M., et al.: Randomized assessment of rapid endovascular treatment of ischemic stroke. N. Engl. J. Med. 372(11), 1019\u20131030 (2015)","journal-title":"N. Engl. J. Med."},{"key":"95_CR2","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1001\/jamaneurol.2018.3661","volume":"76","author":"AM Boers","year":"2019","unstructured":"Boers, A.M., et al.: Mediation of the relationship between endovascular therapy and functional outcome by follow-up infarct volume in patients with acute ischemic stroke. JAMA Neurol. 76, 194\u2013202 (2019)","journal-title":"JAMA Neurol."},{"key":"95_CR3","doi-asserted-by":"publisher","first-page":"679","DOI":"10.3389\/fneur.2018.00679","volume":"9","author":"S Winzeck","year":"2018","unstructured":"Winzeck, S., et al.: ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front. Neurol. 9, 679 (2018)","journal-title":"Front. Neurol."},{"key":"95_CR4","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1016\/j.nicl.2014.03.009","volume":"4","author":"CR Gillebert","year":"2014","unstructured":"Gillebert, C.R., Humphreys, G.W., Mantini, D.: Automated delineation of stroke lesions using brain CT images. NeuroImage Clin. 4, 540\u2013548 (2014)","journal-title":"NeuroImage Clin."},{"key":"95_CR5","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.nicl.2015.06.013","volume":"9","author":"B de Haan","year":"2015","unstructured":"de Haan, B., Clas, P., Juenger, H., Wilke, M., Karnath, H.O.: Fast semi-automated lesion demarcation in stroke. NeuroImage Clin. 9, 69\u201374 (2015)","journal-title":"NeuroImage Clin."},{"key":"95_CR6","doi-asserted-by":"publisher","first-page":"39842","DOI":"10.1109\/ACCESS.2019.2906605","volume":"7","author":"H Kuang","year":"2019","unstructured":"Kuang, H., Menon, B.K., Qiu, W.: Segmenting hemorrhagic and ischemic infarct simultaneously from follow-up non-contrast CT images in patients with acute ischemic stroke. IEEE Access 7, 39842\u201339851 (2019)","journal-title":"IEEE Access"},{"key":"95_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"},{"unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)","key":"95_CR8"},{"unstructured":"Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, \u00c9., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. arXiv preprint arXiv:1812.07032 (2018)","key":"95_CR9"},{"issue":"4","key":"95_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2014.2313433","volume":"33","author":"W Qiu","year":"2014","unstructured":"Qiu, W., Yuan, J., Ukwatta, E., Sun, Y., Rajchl, M., Fenster, A.: Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3D TRUS and MR images. IEEE Trans. Med. Imaging 33(4), 1\u201314 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"95_CR11","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.media.2016.06.038","volume":"35","author":"W Qiu","year":"2017","unstructured":"Qiu, W., et al.: Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images. Med. Image Anal. 35, 181\u2013191 (2017)","journal-title":"Med. Image Anal."},{"key":"95_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/978-3-030-00931-1_78","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Kuang","year":"2018","unstructured":"Kuang, H., Najm, M., Menon, B.K., Qiu, W.: Joint segmentation of intracerebral hemorrhage and infarct from non-contrast CT images of post-treatment acute ischemic stroke patients. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 681\u2013688. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_78"},{"issue":"8","key":"95_CR13","doi-asserted-by":"publisher","first-page":"1522","DOI":"10.3174\/ajnr.A3463","volume":"34","author":"AM Boers","year":"2013","unstructured":"Boers, A.M., et al.: Automated cerebral infarct volume measurement in follow-up noncontrast ct scans of patients with acute ischemic stroke. Am. J. Neuroradiol. 34(8), 1522\u20131527 (2013)","journal-title":"Am. J. Neuroradiol."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_95","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:27:17Z","timestamp":1728520037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_95","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":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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"}]}}