{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:51:14Z","timestamp":1742943074073,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439926"},{"type":"electronic","value":"9783031439933"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43993-3_8","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"77-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Normal Asymmetry Representations for\u00a0Homologous Brain Structures"],"prefix":"10.1007","author":[{"given":"Duilio","family":"Deangeli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel","family":"Iarussi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Pablo","family":"Princich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mariana","family":"Bendersky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ignacio","family":"Larrabide","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Ignacio","family":"Orlando","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"8_CR1","unstructured":"ADNI: Alzheimer\u2019s disease neuroimaging initiative. http:\/\/adni.loni.usc.edu\/ Accessed Feb. 9 2023"},{"key":"8_CR2","unstructured":"IXI dataset website. http:\/\/brain-development.org\/ixidataset\/ Accessed Feb. 9 2023"},{"issue":"2","key":"8_CR3","doi-asserted-by":"publisher","first-page":"276","DOI":"10.3174\/ajnr.A5943","volume":"40","author":"B Ardekani","year":"2019","unstructured":"Ardekani, B., et al.: Sexual dimorphism and hemispheric asymmetry of hippocampal volumetric integrity in normal aging and Alzheimer disease. Am. J. Neuroradiol. 40(2), 276\u2013282 (2019)","journal-title":"Am. J. Neuroradiol."},{"issue":"2","key":"8_CR4","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1093\/brain\/awg034","volume":"126","author":"N Bernasconi","year":"2003","unstructured":"Bernasconi, N., et al.: Mesial temporal damage in temporal lobe epilepsy: a volumetric MRI study of the hippocampus, amygdala and parahippocampal region. Brain 126(2), 462\u2013469 (2003)","journal-title":"Brain"},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Borchert, R., et al.: Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review. medRxiv 2021\u201312 (2021)","DOI":"10.1101\/2021.12.12.21267677"},{"issue":"5","key":"8_CR6","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1176\/appi.ajp.161.5.896","volume":"161","author":"JG Csernansky","year":"2004","unstructured":"Csernansky, J.G., et al.: Abnormalities of thalamic volume and shape in schizophrenia. Am. J. Psychiatry 161(5), 896\u2013902 (2004)","journal-title":"Am. J. Psychiatry"},{"issue":"3","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.3233\/JAD-201116","volume":"79","author":"Z Fu","year":"2021","unstructured":"Fu, Z., et al.: Altered neuroanatomical asymmetries of subcortical structures in subjective cognitive decline, amnestic mild cognitive impairment, and alzheimer\u2019s disease. J. Alzheimers Dis. 79(3), 1121\u20131132 (2021)","journal-title":"J. Alzheimers Dis."},{"key":"8_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24811","volume-title":"Hippocampal Segmentation for Brains with Extensive Atrophy Using Three-dimensional Convolutional Neural Networks","author":"M Goubran","year":"2020","unstructured":"Goubran, M., et al.: Hippocampal Segmentation for Brains with Extensive Atrophy Using Three-dimensional Convolutional Neural Networks. Tech. rep, Wiley Online Library (2020)"},{"issue":"1","key":"8_CR9","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1093\/brain\/awh330","volume":"128","author":"MR Herbert","year":"2005","unstructured":"Herbert, M.R., et al.: Brain asymmetries in autism and developmental language disorder: a nested whole-brain analysis. Brain 128(1), 213\u2013226 (2005)","journal-title":"Brain"},{"issue":"3","key":"8_CR10","doi-asserted-by":"publisher","first-page":"778","DOI":"10.3390\/s21030778","volume":"21","author":"NJ Herzog","year":"2021","unstructured":"Herzog, N.J., Magoulas, G.D.: Brain asymmetry detection and machine learning classification for diagnosis of early dementia. Sensors 21(3), 778 (2021)","journal-title":"Sensors"},{"issue":"5","key":"8_CR11","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1007\/s11682-020-00427-y","volume":"15","author":"A Li","year":"2021","unstructured":"Li, A., Li, F., Elahifasaee, F., Liu, M., Zhang, L.: Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer\u2019s disease diagnosis. Brain Imag. Behav. 15(5), 2330\u20132339 (2021). https:\/\/doi.org\/10.1007\/s11682-020-00427-y","journal-title":"Brain Imag. Behav."},{"key":"8_CR12","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.mri.2019.07.003","volume":"64","author":"CF Liu","year":"2019","unstructured":"Liu, C.F., et al.: Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer\u2019s disease and mild cognitive impairment. Magn. Reson. Imaging 64, 190\u2013199 (2019)","journal-title":"Magn. Reson. Imaging"},{"issue":"1","key":"8_CR13","first-page":"690","volume":"11","author":"A Low","year":"2019","unstructured":"Low, A., et al.: Asymmetrical atrophy of thalamic Subnuclei in Alzheimer\u2019s disease and amyloid-positive mild cognitive impairment is associated with key clinical features. Alzheimer\u2019s Dementia: Diagnosis, Assess. Disease Monit. 11(1), 690\u2013699 (2019)","journal-title":"Alzheimer\u2019s Dementia: Diagnosis, Assess. Disease Monit."},{"issue":"12","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.1162\/jocn.2009.21407","volume":"22","author":"DS Marcus","year":"2010","unstructured":"Marcus, D.S., et al.: Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677\u20132684 (2010)","journal-title":"J. Cogn. Neurosci."},{"issue":"2","key":"8_CR15","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay, O., et al.: Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384\u2013395 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"8_CR16","doi-asserted-by":"publisher","first-page":"201","DOI":"10.3233\/JAD-140189","volume":"43","author":"A de Oliveira","year":"2015","unstructured":"de Oliveira, A., et al.: Defining multivariate normative rules for healthy aging using neuroimaging and machine learning: an application to Alzheimer\u2019s disease. J. Alzheimers Dis. 43(1), 201\u2013212 (2015)","journal-title":"J. Alzheimers Dis."},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Park, B.y, et al.: Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy. Brain 145(4), 1285\u20131298 (2022)","DOI":"10.1093\/brain\/awab417"},{"issue":"5","key":"8_CR18","first-page":"664","volume":"10","author":"O Pedraza","year":"2004","unstructured":"Pedraza, O., Bowers, D., Gilmore, R.: Asymmetry of the hippocampus and amygdala in MRI volumetric measurements of normal adults. JINS 10(5), 664\u2013678 (2004)","journal-title":"JINS"},{"key":"8_CR19","unstructured":"Penny, W.D., et al.: Statistical parametric mapping: the analysis of functional brain images. Elsevier (2011)"},{"key":"8_CR20","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.613967","volume":"12","author":"JP Princich","year":"2021","unstructured":"Princich, J.P., et al.: Diagnostic performance of MRI volumetry in epilepsy patients with hippocampal sclerosis supported through a random forest automatic classification algorithm. Front. Neurol. 12, 613967 (2021)","journal-title":"Front. Neurol."},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Richards, R., et al.: Increased hippocampal shape asymmetry and volumetric ventricular asymmetry in autism spectrum disorder. NeuroImage: Clin. 26, 102207 (2020)","DOI":"10.1016\/j.nicl.2020.102207"},{"key":"8_CR22","unstructured":"Ruff, L., et al.: Deep one-class classification. In: ICML, pp. 4393\u20134402. PMLR (2018)"},{"key":"8_CR23","unstructured":"Sch\u00f6lkopf, B., et al.: Support vector method for novelty detection. Adv. Neural. Inform. Process. Syst. 12 (1999)"},{"key":"8_CR24","unstructured":"Tortora, G.J., Derrickson, B.H.: Principles of anatomy and physiology. John Wiley & Sons (2018)"},{"key":"8_CR25","unstructured":"van Tulder, G.: elasticdeform: Elastic deformations for n-dimensional images (2021)"},{"issue":"12","key":"8_CR26","doi-asserted-by":"publisher","first-page":"3253","DOI":"10.1093\/brain\/aww243","volume":"139","author":"C Wachinger","year":"2016","unstructured":"Wachinger, C., et al.: Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala. Brain 139(12), 3253\u20133266 (2016)","journal-title":"Brain"},{"issue":"1","key":"8_CR27","first-page":"48","volume":"201","author":"AA Woolard","year":"2012","unstructured":"Woolard, A.A., Heckers, S.: Anatomical and functional correlates of human hippocampal volume asymmetry. Psych. Res.: Neuroimag. 201(1), 48\u201353 (2012)","journal-title":"Psych. Res.: Neuroimag."},{"key":"8_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2021.04.020","volume":"148","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Deng, X.: Anomaly detection using improved deep SVDD model with data structure preservation. Pattern Recogn. Lett. 148, 1\u20136 (2021)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43993-3_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T16:13:42Z","timestamp":1712074422000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43993-3_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439926","9783031439933"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43993-3_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"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":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"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)"}}]}}