{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:27:27Z","timestamp":1775071647339,"version":"3.50.1"},"reference-count":40,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["CUHK 14113214"],"award-info":[{"award-number":["CUHK 14113214"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["CUHK 14204117"],"award-info":[{"award-number":["CUHK 14204117"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","award":["GHP-025-17SZ"],"award-info":[{"award-number":["GHP-025-17SZ"]}],"id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003452","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","award":["GHP-028-14SZ"],"award-info":[{"award-number":["GHP-028-14SZ"]}],"id":[{"id":"10.13039\/501100003452","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1109\/tmi.2018.2821244","type":"journal-article","created":{"date-parts":[[2018,3,30]],"date-time":"2018-03-30T18:09:29Z","timestamp":1522433369000},"page":"2149-2160","source":"Crossref","is-referenced-by-count":180,"title":["Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8103-5210","authenticated-orcid":false,"given":"Rongzhao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5125-974X","authenticated-orcid":false,"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6844-2847","authenticated-orcid":false,"given":"Wutao","family":"Lou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jill M.","family":"Abrigo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vincent C. T.","family":"Mok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4962-4132","authenticated-orcid":false,"given":"Winnie C. W.","family":"Chu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Defeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1161\/01.STR.24.1.35"},{"key":"ref33","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"nair","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref32","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"},{"key":"ref31","author":"veit","year":"2016","journal-title":"Residual networks are exponential ensembles of relatively shallow networks"},{"key":"ref30","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref37","first-page":"562","article-title":"Deeply-supervised nets","author":"lee","year":"2015","journal-title":"Proc Artif Intell Stat"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref35","first-page":"5","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Proc NIPS Workshop on Deep Learning and Unsupervised Feature Learning"},{"key":"ref34","article-title":"Learning multiple layers features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.07.009"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12112134"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"ref12","author":"goodfellow","year":"2016","journal-title":"Deep Learning"},{"key":"ref13","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535865"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528129"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528162"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref28","first-page":"666","article-title":"Shallow vs. deep sum-product networks","author":"delalleau","year":"2011","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","first-page":"38e","article-title":"Heart disease and stroke statistics—2016 update: A report from the American Heart Association","volume":"133","author":"mozaffarian","year":"2016","journal-title":"Circulation"},{"key":"ref27","author":"h\u00e5stad","year":"1987","journal-title":"Computational Limitations of Small-depth Circuits"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1093\/ageing\/afn281"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2006.09.045"},{"key":"ref29","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proc 13th Int Conf Artif Intell Stat"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2014.01.003"},{"key":"ref8","first-page":"3762","article-title":"A novel framework for segmentation of stroke lesions in diffusion weighted MRI using multiple b-value data","author":"mujumdar","year":"2012","journal-title":"Proc Int Conf Pattern Recognit (ICPR)"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2007.4352736"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMcp072057"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2017.06.016"},{"key":"ref1","year":"2017","journal-title":"The Top 10 Causes of Death"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref21","first-page":"424","article-title":"3D U-NET: Learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref23","first-page":"66","article-title":"Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images","author":"yu","year":"2017","journal-title":"Proc AAAI"},{"key":"ref26","author":"drozdzal","year":"2017","journal-title":"Learning normalized inputs for iterative estimation in medical image segmentation"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/8452189\/08328863.pdf?arnumber=8328863","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T15:57:55Z","timestamp":1643212675000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8328863\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9]]},"references-count":40,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2018.2821244","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9]]}}}