{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T13:01:56Z","timestamp":1760014916821,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031448577"},{"type":"electronic","value":"9783031448584"}],"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\/978-3-031-44858-4_14","type":"book-chapter","created":{"date-parts":[[2023,10,7]],"date-time":"2023-10-07T05:01:55Z","timestamp":1696654915000},"page":"143-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Attention Assisted Multi-resolution Networks for\u00a0the\u00a0Segmentation of\u00a0White Matter Hyperintensities in\u00a0Postmortem MRI Scans"],"prefix":"10.1007","author":[{"given":"Anoop","family":"Benet Nirmala","sequence":"first","affiliation":[]},{"given":"Tanweer","family":"Rashid","sequence":"additional","affiliation":[]},{"given":"Elyas","family":"Fadaee","sequence":"additional","affiliation":[]},{"given":"Nicolas","family":"Honnorat","sequence":"additional","affiliation":[]},{"given":"Karl","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sokratis","family":"Charisis","sequence":"additional","affiliation":[]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Aishwarya","family":"Vemula","sequence":"additional","affiliation":[]},{"given":"Jinqi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Peter","family":"Fox","sequence":"additional","affiliation":[]},{"given":"Timothy E.","family":"Richardson","sequence":"additional","affiliation":[]},{"given":"Jamie M.","family":"Walker","sequence":"additional","affiliation":[]},{"given":"Kevin","family":"Bieniek","sequence":"additional","affiliation":[]},{"given":"Sudha","family":"Seshadri","sequence":"additional","affiliation":[]},{"given":"Mohamad","family":"Habes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Anbeek, P., Vincken, K.L., Van Osch, M.J.P., Bisschops, R.H.C., Van Det Grond, J.: Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage 21(3), 1037\u20131044 (2004)","DOI":"10.1016\/j.neuroimage.2003.10.012"},{"key":"14_CR2","unstructured":"Anonymous: in revision"},{"issue":"4","key":"14_CR3","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s12021-011-9109-y","volume":"9","author":"B Avants","year":"2011","unstructured":"Avants, B., Tustison, N., Wu, J., Cook, P., Gee, J.: An open source multivariate framework for n-tissue segmentation with evaluation on public data. Neuroinformatics 9(4), 381\u2013400 (2011)","journal-title":"Neuroinformatics"},{"issue":"1","key":"14_CR4","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1212\/WNL.58.1.48","volume":"58","author":"RR Benson","year":"2002","unstructured":"Benson, R.R., et al.: Older people with impaired mobility have specific loci of periventricular abnormality on MRI. Neurology 58(1), 48\u201355 (2002)","journal-title":"Neurology"},{"key":"14_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Fiford, C.M., et al.: Automated white matter hyperintensity segmentation using bayesian model selection: assessment and correlations with cognitive change. Neuroinformatics 18, 429\u2013449 (2020)","DOI":"10.1007\/s12021-019-09439-6"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Gibson, E., Gao, F., Black, S.E., Lobaugh, N.J.: Automatic segmentation of white matter hyperintensities in the elderly using flair images at 3t. J. Magn. Reson. Imaging 31(6), 1311\u20131322 (2010)","DOI":"10.1002\/jmri.22004"},{"key":"14_CR8","unstructured":"Giese, A.K., et al.: White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype. Neurology 95(1), e79\u2013e88 (2020)"},{"issue":"1\u20132","key":"14_CR9","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1016\/j.jns.2009.02.327","volume":"283","author":"L Grinberg","year":"2009","unstructured":"Grinberg, L., et al.: Improved detection of incipient vascular changes by a biotechnological platform combining post mortem MRI in situ with neuropathology. J. Neurol. Sci. 283(1\u20132), 2\u20138 (2009)","journal-title":"J. Neurol. Sci."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Habes, M., et al.: White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain 139(4), 1164\u20131179 (2016)","DOI":"10.1093\/brain\/aww008"},{"issue":"10","key":"14_CR11","doi-asserted-by":"publisher","first-page":"e964","DOI":"10.1212\/WNL.0000000000006116","volume":"91","author":"M Habes","year":"2018","unstructured":"Habes, M., et al.: White matter lesions: spatial heterogeneity, links to risk factors, cognition, genetics, and atrophy. Neurology 91(10), e964\u2013e975 (2018)","journal-title":"Neurology"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"14_CR13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz, N., Rahman, M.S.: Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw. 121, 74\u201387 (2020)","journal-title":"Neural Netw."},{"issue":"2","key":"14_CR14","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C., Behrens, T., Woolrich, M., Smith, S.: FSL. NeuroImage 62(2), 782\u2013790 (2012)","journal-title":"NeuroImage"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Jha, D., Riegler, M.A., Johansen, D., Halvorsen, P., Johansen, H.D.: Doubleu-net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), pp. 558\u2013564. IEEE (2020)","DOI":"10.1109\/CBMS49503.2020.00111"},{"key":"14_CR16","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s12264-019-00342-3","volume":"35","author":"LE Jonkman","year":"2019","unstructured":"Jonkman, L.E., Kenkhuis, B., Geurts, J.J., van de Berg, W.D.: Post-mortem MRI and histopathology in neurologic disease: a translational approach. Neurosci. Bull. 35, 229\u2013243 (2019)","journal-title":"Neurosci. Bull."},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Lee, S., et al.: White matter hyperintensities are a core feature of Alzheimer\u2019s disease: evidence from the dominantly inherited Alzheimer network. Ann. Neurol. 79(6), 929\u2013939 (2016)","DOI":"10.1007\/s00415-008-0612-5"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Maldjian, J.A., et al.: Automated white matter total lesion volume segmentation in diabetes. Am. J. Neuroradiol. 34(12), 2265\u20132270 (2013)","DOI":"10.3174\/ajnr.A3590"},{"issue":"1","key":"14_CR19","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1002\/jmri.22003","volume":"31","author":"J Manj\u00f3n","year":"2010","unstructured":"Manj\u00f3n, J., Coup\u00e9, P., Mart\u00ed-Bonmat\u00ed, L., Collins, D., Robles, M.: Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Resonan. Imaging: JMRI 31(1), 192\u2013203 (2010)","journal-title":"J. Magn. Resonan. Imaging: JMRI"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"Murray, M.E., et al.: A quantitative postmortem MRI design sensitive to white matter hyperintensity differences and their relationship with underlying pathology. J. Neuropathol. Exp. Neurol. 71(12), 1113\u20131122 (2012)","DOI":"10.1097\/NEN.0b013e318277387e"},{"key":"14_CR21","unstructured":"Oktay, O., et al.: Attention U-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Rahil, M., Anoop, B., Girish, G., Kothari, A.R., Koolagudi, S.G., Rajan, J.: A deep ensemble learning-based CNN architecture for multiclass retinal fluid segmentation in oct images. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3244922"},{"issue":"1","key":"14_CR23","doi-asserted-by":"publisher","first-page":"14124","DOI":"10.1038\/s41598-021-93427-x","volume":"11","author":"T Rashid","year":"2021","unstructured":"Rashid, T., et al.: DeepMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI. Sci. Rep. 11(1), 14124 (2021)","journal-title":"Sci. Rep."},{"key":"14_CR24","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"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"Roseborough, A.D., et al.: Post-mortem 7 tesla MRI detection of white matter hyperintensities: a multidisciplinary voxel-wise comparison of imaging and histological correlates. NeuroImage: Clin. 27, 102340 (2020)","DOI":"10.1016\/j.nicl.2020.102340"},{"issue":"11","key":"14_CR26","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0048953","volume":"7","author":"T Samaille","year":"2012","unstructured":"Samaille, T., et al.: Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PLoS ONE 7(11), e48953 (2012)","journal-title":"PLoS ONE"},{"issue":"7","key":"14_CR27","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1016\/j.mri.2012.12.004","volume":"31","author":"R Sim\u00f5es","year":"2013","unstructured":"Sim\u00f5es, R., et al.: Automatic segmentation of cerebral white matter hyperintensities using only 3d flair images. Magn. Reson. Imaging 31(7), 1182\u20131189 (2013)","journal-title":"Magn. Reson. Imaging"},{"issue":"4","key":"14_CR28","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1212\/WNL.54.4.838","volume":"54","author":"CD Smith","year":"2000","unstructured":"Smith, C.D., Snowdon, D.A., Wang, H., Markesbery, W.R.: White matter volumes and periventricular white matter hyperintensities in aging and dementia. Neurology 54(4), 838\u2013842 (2000)","journal-title":"Neurology"},{"issue":"6","key":"14_CR29","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4ITK: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"14_CR30","unstructured":"Verhaaren, B.F., et al.: Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI. Circulat. Cardiovasc. Genet. 8(2), 398\u2013409 (2015)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Viteri, J.A., Loayza, F.R., Pelaez, E., Layedra, F.: Automatic brain white matter hypertinsities segmentation using deep learning techniques. In: HEALTHINF, pp. 244\u2013252 (2021)","DOI":"10.5220\/0010360302440252"},{"issue":"12","key":"14_CR32","doi-asserted-by":"publisher","first-page":"2141","DOI":"10.4103\/1673-5374.241465","volume":"13","author":"FJ Zhuang","year":"2018","unstructured":"Zhuang, F.J., Chen, Y., He, W.B., Cai, Z.Y.: Prevalence of white matter hyperintensities increases with age. Neural Regen. Res. 13(12), 2141 (2018)","journal-title":"Neural Regen. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44858-4_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T16:45:54Z","timestamp":1710261954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44858-4_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031448577","9783031448584"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44858-4_14","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":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","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":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnworkshop.github.io\/","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":"27","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":"16","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":"59% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}