{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T23:23:23Z","timestamp":1781306603221,"version":"3.54.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81471731"],"award-info":[{"award-number":["81471731"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81622025"],"award-info":[{"award-number":["81622025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2993504","type":"journal-article","created":{"date-parts":[[2020,5,8]],"date-time":"2020-05-08T19:59:15Z","timestamp":1588967955000},"page":"97032-97044","source":"Crossref","is-referenced-by-count":7,"title":["Hippocampus Segmentation for Preterm and Aging Brains Using 3D Densely Connected Fully Convolutional Networks"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0138-1314","authenticated-orcid":false,"given":"Debin","family":"Zeng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6989-2981","authenticated-orcid":false,"given":"Qiongling","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6461-1244","authenticated-orcid":false,"given":"Baoqiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3459-6821","authenticated-orcid":false,"given":"Shuyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref38","first-page":"562","article-title":"Deeply-supervised nets","author":"lee","year":"2015","journal-title":"Proc Artif Intell Statist (AISTATS)"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24811"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.02.009"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.12.002"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2005.06.003"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.05.083"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950499"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67389-9_11"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2906727"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awn227"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.21367"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.08.005"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/srep45501"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363544"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.11.004"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-017-5581-1"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10470-6_39"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-02267-3_1"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2696121"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/MCE.2016.2640698"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2015.07.019"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1037\/0033-2909.86.2.420"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref52","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2018.02.005"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0059990"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.05.001"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2322280"},{"key":"ref13","article-title":"Initialisation of 3D level set for hippocampus segmentation from volumetric brain MR images","author":"hajiesmaeili","year":"2014","journal-title":"Proc 6th Int Conf Digit Image Process (ICDIP)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.22183"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2009.02.013"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.10.002"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.02.069"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.05.082"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_8"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaac.2011.11.009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8749.2011.04150.x"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2012.27"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/BF00308809"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.biopsych.2008.08.007"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-04847-9"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1111\/cns.12415"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00003"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref42","first-page":"1","article-title":"How does batch normalization help optimization? (no, it is not about internal covariate shift)","author":"santurkar","year":"2018","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref41","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc Artif Intell Statist (AISTATS)"},{"key":"ref44","article-title":"A guide to convolution arithmetic for deep learning","author":"dumoulin","year":"2016","journal-title":"ArXiv 1603 07285"},{"key":"ref43","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn (ICML)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09090157.pdf?arnumber=9090157","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:51:57Z","timestamp":1639770717000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9090157\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2993504","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}