{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T19:13:26Z","timestamp":1743102806461,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031456756"},{"type":"electronic","value":"9783031456763"}],"license":[{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,15]],"date-time":"2023-10-15T00:00:00Z","timestamp":1697328000000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-45676-3_33","type":"book-chapter","created":{"date-parts":[[2023,10,14]],"date-time":"2023-10-14T08:02:16Z","timestamp":1697270536000},"page":"325-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Ellipsoid Bounding Shapes and\u00a0Fast R-CNN for\u00a0Enlarged Perivascular Spaces Detection and\u00a0Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-2318","authenticated-orcid":false,"given":"Mariam","family":"Zabihi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chayanin","family":"Tangwiriyasakul","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silvia","family":"Ingala","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luigi","family":"Lorenzini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Camarasa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3543-3706","authenticated-orcid":false,"given":"Frederik","family":"Barkhof","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marleen","family":"de Bruijne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Jorge","family":"Cardoso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5753-428X","authenticated-orcid":false,"given":"Carole H.","family":"Sudre","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,15]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Bown C.W: Physiology and clinical relevance of enlarged perivascular spaces in the aging brain. Neurology 98(3), 107\u2013117 (2022)","DOI":"10.1212\/WNL.0000000000013077"},{"issue":"11","key":"33_CR2","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1212\/WNL.0000000000011537","volume":"96","author":"M Paradise","year":"2021","unstructured":"Paradise, M.: Association of dilated perivascular spaces with cognitive decline and incident dementia. Neurology 96(11), 1501\u20131511 (2021)","journal-title":"Neurology"},{"issue":"9","key":"33_CR3","doi-asserted-by":"publisher","first-page":"1105","DOI":"10.1001\/jamaneurol.2017.1397","volume":"74","author":"J Ding","year":"2017","unstructured":"Ding, J.: Large perivascular spaces visible on magnetic resonance imaging, cerebral small vessel disease progression, and risk of dementia: the age, gene\/environment susceptibility-Reykjavik study. JAMA Neurol. 74(9), 1105\u20131112 (2017)","journal-title":"JAMA Neurol."},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"TS Asgari","year":"2021","unstructured":"Asgari, T.S.: Deep semantic segmentation of natural and medical images: a review. Artif. Intell. Rev. 54, 137\u2013178 (2021)","journal-title":"Artif. Intell. Rev."},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Ranjbarzadeh R. : Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 152 (2023)","DOI":"10.1016\/j.compbiomed.2022.106405"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Ribli D.: Detecting and classifying lesions in mammograms with Deep Learning. Sci. Rep. 8(1), 4165 (2018)","DOI":"10.1038\/s41598-018-22437-z"},{"issue":"1","key":"33_CR7","first-page":"1","volume":"12","author":"B Williamson","year":"2023","unstructured":"Williamson, B.: Automated grading of enlarged perivascular spaces in clinical imaging data of an acute stroke cohort using an interpretable, 3D deep learning framework. Sci. Rep. 12(1), 1\u20137 (2023)","journal-title":"Sci. Rep."},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Dubost, F.: Enlarged perivascular spaces in brain MRI: automated quantification in four regions. NeuroImage 185, 534\u2013544 (2019)","DOI":"10.1016\/j.neuroimage.2018.10.026"},{"key":"33_CR9","doi-asserted-by":"crossref","unstructured":"Rashid, T.: Deep learning based detection of enlarged perivascular spaces on brain MRI. Neuroimage Rep. 3(1), 100162 (2023)","DOI":"10.1016\/j.ynirp.2023.100162"},{"key":"33_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-030-32251-9_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"KMH van Wijnen","year":"2019","unstructured":"van Wijnen, K.M.H., et al.: Automated lesion detection by\u00a0regressing intensity-based distance with\u00a0a neural network. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 234\u2013242. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_26"},{"key":"33_CR11","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":"33_CR12","doi-asserted-by":"crossref","unstructured":"Fu, GH.: Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data. BMC Bioinform. 21(121) (2020)","DOI":"10.1186\/s12859-020-3411-3"},{"key":"33_CR13","unstructured":"Sudre, Carole H.: Where is VALDO? VAscular lesions detection and segmentation challenge at MICCAI 2021. arXiv preprint arXiv:2208.07167 (2022)"},{"key":"33_CR14","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/s41582-020-0312-z","volume":"16","author":"JM Wardlaw","year":"2020","unstructured":"Wardlaw, J.M.: Perivascular spaces in the brain: anatomy, physiology and pathology. Nat. Rev. Neurol. 16, 137\u2013153 (2020)","journal-title":"Nat. Rev. Neurol."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45676-3_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T11:54:13Z","timestamp":1710330853000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45676-3_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,15]]},"ISBN":["9783031456756","9783031456763"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45676-3_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,10,15]]},"assertion":[{"value":"15 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2023?pli=1","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":"139","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":"93","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":"67% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}