{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:26:56Z","timestamp":1769833616180,"version":"3.49.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164309","type":"print"},{"value":"9783031164316","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16431-6_13","type":"book-chapter","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T21:02:58Z","timestamp":1663189378000},"page":"133-142","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["One-Shot Segmentation of\u00a0Novel White Matter Tracts via\u00a0Extensive Data Augmentation"],"prefix":"10.1007","author":[{"given":"Wan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhizheng","family":"Zhuo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuyang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"issue":"8","key":"13_CR1","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.1002\/hbm.25378","volume":"42","author":"L Banihashemi","year":"2021","unstructured":"Banihashemi, L., et al.: Opposing relationships of childhood threat and deprivation with stria terminalis white matter. Hum. Brain Mapp. 42(8), 2445\u20132460 (2021)","journal-title":"Hum. Brain Mapp."},{"issue":"4","key":"13_CR2","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1002\/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O","volume":"44","author":"PJ Basser","year":"2000","unstructured":"Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4), 625\u2013632 (2000)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"13_CR3","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1016\/j.neuroimage.2011.06.020","volume":"58","author":"PL Bazin","year":"2011","unstructured":"Bazin, P.L., et al.: Direct segmentation of the major white matter tracts in diffusion tensor images. Neuroimage 58(2), 458\u2013468 (2011)","journal-title":"Neuroimage"},{"key":"13_CR4","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Ding, Y., Yu, X., Yang, Y.: Modeling the probabilistic distribution of unlabeled data for one-shot medical image segmentation. In: AAAI Conference on Artificial Intelligence, pp. 1246\u20131254. AAAI (2021)","DOI":"10.1609\/aaai.v35i2.16212"},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"13_CR7","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.neuroimage.2014.07.061","volume":"103","author":"B Jeurissen","year":"2014","unstructured":"Jeurissen, B., Tournier, J.D., Dhollander, T., Connelly, A., Sijbers, J.: Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage 103, 411\u2013426 (2014)","journal-title":"Neuroimage"},{"key":"13_CR8","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"13_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.118934","volume":"250","author":"W Liu","year":"2022","unstructured":"Liu, W., et al.: Volumetric segmentation of white matter tracts with label embedding. Neuroimage 250, 118934 (2022)","journal-title":"Neuroimage"},{"key":"13_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/978-3-030-78191-0_17","volume-title":"Information Processing in Medical Imaging","author":"Q Lu","year":"2021","unstructured":"Lu, Q., Ye, C.: Knowledge transfer for few-shot segmentation of novel white matter tracts. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 216\u2013227. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_17"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"MacNiven, K.H., Leong, J.K., Knutson, B.: Medial forebrain bundle structure is linked to human impulsivity. Sci. Adv. 6(38), eaba4788 (2020)","DOI":"10.1126\/sciadv.aba4788"},{"issue":"1","key":"13_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.schres.2014.09.007","volume":"161","author":"LJ O\u2019Donnell","year":"2015","unstructured":"O\u2019Donnell, L.J., Pasternak, O.: Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls. Schizophr. Res. 161(1), 133\u2013141 (2015)","journal-title":"Schizophr. Res."},{"issue":"12","key":"13_CR13","doi-asserted-by":"publisher","first-page":"6152","DOI":"10.1093\/cercor\/bhaa170","volume":"30","author":"RL Stephens","year":"2020","unstructured":"Stephens, R.L., Langworthy, B.W., Short, S.J., Girault, J.B., Styner, M.A., Gilmore, J.H.: White matter development from birth to 6 years of age: a longitudinal study. Cereb. Cortex 30(12), 6152\u20136168 (2020)","journal-title":"Cereb. Cortex"},{"issue":"5","key":"13_CR14","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1093\/cercor\/bhaa377","volume":"31","author":"SM Toescu","year":"2021","unstructured":"Toescu, S.M., Hales, P.W., Kaden, E., Lacerda, L.M., Aquilina, K., Clark, C.A.: Tractographic and microstructural analysis of the dentato-rubro-thalamo-cortical tracts in children using diffusion MRI. Cereb. Cortex 31(5), 2595\u20132609 (2021)","journal-title":"Cereb. Cortex"},{"issue":"4","key":"13_CR15","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1016\/j.neuroimage.2007.02.016","volume":"35","author":"JD Tournier","year":"2007","unstructured":"Tournier, J.D., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4), 1459\u20131472 (2007)","journal-title":"Neuroimage"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Van Essen","year":"2013","unstructured":"Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., Ugurbil, K.: Wu-Minn HCP consortium: the WU-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","journal-title":"Neuroimage"},{"key":"13_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neuroimage.2018.07.070","volume":"183","author":"J Wasserthal","year":"2018","unstructured":"Wasserthal, J., Neher, P., Maier-Hein, K.H.: TractSeg - fast and accurate white matter tract segmentation. Neuroimage 183, 239\u2013253 (2018)","journal-title":"Neuroimage"},{"key":"13_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-030-59728-3_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"Y Wu","year":"2020","unstructured":"Wu, Y., Hong, Y., Ahmad, S., Lin, W., Shen, D., Yap, Pew-Thian.: Tract dictionary learning for fast and robust recognition of fiber bundles. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 251\u2013259. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_25"},{"issue":"3","key":"13_CR19","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12021-015-9264-7","volume":"13","author":"C Ye","year":"2015","unstructured":"Ye, C., Yang, Z., Ying, S.H., Prince, J.L.: Segmentation of the cerebellar peduncles using a random forest classifier and a multi-object geometric deformable model: application to spinocerebellar ataxia type 6. Neuroinformatics 13(3), 367\u2013381 (2015)","journal-title":"Neuroinformatics"},{"issue":"11","key":"13_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0049790","volume":"7","author":"JD Yeatman","year":"2012","unstructured":"Yeatman, J.D., Dougherty, R.F., Myall, N.J., Wandell, B.A., Feldman, H.M.: Tract profiles of white matter properties: automating fiber-tract quantification. PLoS ONE 7(11), e49790 (2012)","journal-title":"PLoS ONE"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: CutMix: regularization strategy to train strong classifiers with localizable features. In: International Conference on Computer Vision, pp. 6023\u20136032. IEEE (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"issue":"14","key":"13_CR22","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1212\/WNL.0000000000009014","volume":"94","author":"A Zarkali","year":"2020","unstructured":"Zarkali, A., McColgan, P., Leyland, L.A., Lees, A.J., Rees, G., Weil, R.S.: Fiber-specific white matter reductions in Parkinson hallucinations and visual dysfunction. Neurology 94(14), 1525\u20131538 (2020)","journal-title":"Neurology"},{"key":"13_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101761","volume":"65","author":"F Zhang","year":"2020","unstructured":"Zhang, F., Karayumak, S.C., Hoffmann, N., Rathi, Y., Golby, A.J., O\u2019Donnell, L.J.: Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Med. Image Anal. 65, 101761 (2020)","journal-title":"Med. Image Anal."},{"key":"13_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1007\/978-3-030-87193-2_19","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Zhang","year":"2021","unstructured":"Zhang, X., et al.: CarveMix: a simple data augmentation method for brain lesion segmentation. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 196\u2013205. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_19"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16431-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:13:45Z","timestamp":1710360825000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16431-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164309","9783031164316"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16431-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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","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)"}}]}}