{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:51:28Z","timestamp":1786038688008,"version":"3.56.0"},"reference-count":45,"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":[{"name":"the science and technology project of Sichuan","award":["2019YFG0504"],"award-info":[{"award-number":["2019YFG0504"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872066 and U19A2078"],"award-info":[{"award-number":["61872066 and U19A2078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the science and technology service industry demonstration project of Sichuan","award":["2019GFW126"],"award-info":[{"award-number":["2019GFW126"]}]},{"name":"Sichuan science and technology innovation project","award":["2019006"],"award-info":[{"award-number":["2019006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.2994592","type":"journal-article","created":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T03:02:10Z","timestamp":1589511730000},"page":"1-1","source":"Crossref","is-referenced-by-count":25,"title":["A SYMMETRIC FULLY CONVOLUTIONAL RESIDUAL NETWORK WITH DCRF FOR ACCURATE TOOTH SEGMENTATION"],"prefix":"10.1109","author":[{"given":"Yunbo","family":"Rao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanman","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiansu","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihong","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qifei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2018.2839685"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2708714"},{"key":"ref33","article-title":"Automatic lumbar spinal CT image segmentation with a dual densely connected U-Net","author":"tang","year":"2019","journal-title":"arXiv 1910 09198"},{"key":"ref32","first-page":"385","article-title":"Automatic brain structures segmentation using deep residual dilated u-net","author":"li","year":"2018","journal-title":"Proc MICCAI"},{"key":"ref31","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc MICCAI"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00866"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-34110-7_43"},{"key":"ref36","article-title":"FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics","author":"quan","year":"2016","journal-title":"arXiv 1612 05360"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2924262"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00223"},{"key":"ref10","first-page":"497","article-title":"Learning contextual and attentive information for brain tumor segmentation","author":"zhou","year":"2018","journal-title":"International MICCAI Brainlesion Workshop"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2016.11.003"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-008-0230-9"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126219"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2014.04.006"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2016.10.002"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1118\/1.4901521"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"3243","DOI":"10.1109\/TIP.2010.2069690","article-title":"Distance regularized level set evolution and its application to image segmentation","volume":"19","author":"li","year":"2010","journal-title":"IEEE Trans Image Process"},{"key":"ref17","article-title":"Combining the best of graphical models and ConvNets for semantic segmentation","author":"cogswell","year":"2014","journal-title":"arXiv 1412 4313"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.45"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref4","article-title":"Fully convolutional instance-aware semantic segmentation","author":"li","year":"2016","journal-title":"arXiv 1611 07709"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.2747"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.01.010"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.273"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2709406"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01270"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.343"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00963"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_32"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.778"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2018.8545571"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-019-2685-1"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00653"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00902"},{"key":"ref44","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc NIPS"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_46"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-018-0556-3"},{"key":"ref25","article-title":"Multi-level contextual network for biomedical image segmentation","author":"dadashzadeh","year":"2018","journal-title":"arXiv 1810 00327"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/6514899\/09093915.pdf?arnumber=9093915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:52:05Z","timestamp":1639770725000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9093915\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2020.2994592","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]}}}