{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:08:35Z","timestamp":1774541315194,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":["61872261"],"award-info":[{"award-number":["61872261"]}],"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":["61972274"],"award-info":[{"award-number":["61972274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011160","name":"Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University","doi-asserted-by":"publisher","award":["2018-VRLAB2018B07"],"award-info":[{"award-number":["2018-VRLAB2018B07"]}],"id":[{"id":"10.13039\/501100011160","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004480","name":"Research Project Supported by Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["201801D121139"],"award-info":[{"award-number":["201801D121139"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2020.3047429","type":"journal-article","created":{"date-parts":[[2020,12,25]],"date-time":"2020-12-25T21:10:23Z","timestamp":1608930623000},"page":"2864-2878","source":"Crossref","is-referenced-by-count":15,"title":["Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN"],"prefix":"10.1109","volume":"9","author":[{"given":"Xiaojiao","family":"Xiao","sequence":"first","affiliation":[]},{"given":"Yan","family":"Qiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2444-4004","authenticated-orcid":false,"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xingyu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaotang","family":"Yang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_18"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/4185279"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2536809"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2548501"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528120"},{"key":"ref37","article-title":"Automatic liver lesion segmentation using a deep convolutional neural network method","author":"han","year":"2017","journal-title":"arXiv 1704 07239"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2017.03.008"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46976-8_9"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref27","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14141180"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00268-005-0718-1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.5120\/13169-0708"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.01.002"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2017.02.003"},{"key":"ref24","first-page":"4675","article-title":"Random feature subspace ensemble based extreme learning machine for liver tumor detection and segmentation","author":"huang","year":"2014","journal-title":"Proc 36th Annu Int Conf IEEE Eng Med Biol Soc"},{"key":"ref23","first-page":"2015","article-title":"Liver tumor segmentation using single level set method with shape and intensity prior","volume":"10","author":"amarajothi","year":"2015","journal-title":"Int J Appl Eng Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CITS.2017.8035318"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2011.6091484"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2013851"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-9538-4_22"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2018.19.4.568"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1097\/00004728-200105000-00001"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2036.2007.03498.x"},{"key":"ref52","article-title":"Neural network-based automatic liver tumor segmentation with random forest-based candidate filtering","author":"chlebus","year":"2017","journal-title":"arXiv 1706 00842"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_27"},{"key":"ref11","first-page":"109","article-title":"Shape constrained automatic segmentation of the liver based on a heuristic intensity model","author":"kainm\u00fcller","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489136"},{"key":"ref12","first-page":"235","article-title":"Liver segmentation in CT data: A segmentation refinement approach","author":"beichel","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref13","first-page":"29","article-title":"A hybrid user interface for manipulation of volumetric medical data","volume":"6","author":"bornik","year":"2006","journal-title":"Proc 3DUI"},{"key":"ref14","first-page":"225","article-title":"Hepatux&#x2014;A semiautomatic liver segmentation system","author":"beck","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref15","first-page":"215","article-title":"Semi-automatic segmentation of the liver and its evaluation on the MICCAI 2007 grand challenge data set","author":"dawant","year":"2007","journal-title":"3-D Segmentation In The Clinic A Grand Challenge"},{"key":"ref16","first-page":"189","article-title":"Efficient liver segmentation exploiting level-set speed images with 2.5 D shape propagation","author":"lee","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref17","first-page":"179","article-title":"Two-stage semi-automatic organ segmentation framework using radial basis functions and level sets","author":"wimmer","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref18","first-page":"197","article-title":"Atlas based liver segmentation using nonrigid registration with a b-spline transformation model","author":"slagmolen","year":"2007","journal-title":"Proc MICCAI Workshop on 3-D Segmentation in the Clinic A Grand Challenge"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2015.06.025"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2431062144"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.24419"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1016\/j.jhep.2011.12.001","article-title":"European association for the study of the liver European organisation for research and treatment of cancer: EASL-EORTC clinical practice guidelines: Management of hepatocellular carcinoma","volume":"56","author":"llovet","year":"2012","journal-title":"J Hepatol"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-011-2225-0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.09.001"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14132361"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26178"},{"key":"ref46","first-page":"2377","article-title":"Training very deep networks","author":"srivastava","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101668"},{"key":"ref48","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2876633"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"ref44","first-page":"2672","article-title":"Generative adversarial Nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101667"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09309035.pdf?arnumber=9309035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:47Z","timestamp":1639770947000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9309035\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3047429","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}