{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:02:11Z","timestamp":1776729731483,"version":"3.51.2"},"reference-count":34,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2956550","type":"journal-article","created":{"date-parts":[[2019,12,4]],"date-time":"2019-12-04T23:03:52Z","timestamp":1575500632000},"page":"172871-172878","source":"Crossref","is-referenced-by-count":16,"title":["Pancreatic Segmentation via Ringed Residual U-Net"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9191-3133","authenticated-orcid":false,"given":"Lin","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3583-4975","authenticated-orcid":false,"given":"Liqiong","family":"Jian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4475-2338","authenticated-orcid":false,"given":"Jun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8469-5302","authenticated-orcid":false,"given":"Bin","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.01.006"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.03.001"},{"key":"ref31","first-page":"1","article-title":"Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function","author":"cai","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref30","article-title":"CBAM: Convolutional block attention module","author":"woo","year":"2018","journal-title":"arXiv 1807 06521"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33454-2_9"},{"key":"ref11","first-page":"666","article-title":"Geodesic patch-based segmentation","author":"wang","year":"2014","journal-title":"Medical Image Computing and Computer-Assisted Intervention—MICCAI"},{"key":"ref12","first-page":"165","article-title":"Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images","author":"chu","year":"2013","journal-title":"Medical Image Computing and Computer-Assisted Intervention—MICCAI"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2265805"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref16","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":"ref17","first-page":"693","article-title":"A fixed-point model for pancreas segmentation in abdominal CT scans","author":"zhou","year":"2017","journal-title":"Proc Int Conf Med Image Comput Comput Assist Intervent"},{"key":"ref18","first-page":"451","article-title":"Spatial aggregation of holistically-nested networks for automated pancreas segmentation","author":"roth","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput Assist Intervent"},{"key":"ref19","article-title":"Pancreas segmentation in CT and MRI images via domain specific network designing and recurrent neural contextual learning","author":"cai","year":"2018","journal-title":"arXiv 1803 11303"},{"key":"ref28","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"chollet","year":"2016","journal-title":"arXiv 1610 02357"},{"key":"ref4","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","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":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363626"},{"key":"ref29","article-title":"Aggregated residual transformations for deep neural networks","author":"xie","year":"2016","journal-title":"arXiv 1611 05431"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.4236\/jcc.2015.311023"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820742"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2337057"},{"key":"ref2","first-page":"222","article-title":"Deep supervision for pancreatic cyst segmentation in abdominal CT scans","author":"zhou","year":"2017","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.2004421"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2018.00083"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref21","first-page":"480","article-title":"A novel Bayesian model incorporating deep neural network and statistical shape model for pancreas segmentation","author":"ma","year":"2018","journal-title":"Proc Int Conf Med Image Comput Comput Assist Intervent"},{"key":"ref24","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref23","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"krizhevsky","year":"2009"},{"key":"ref26","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08917626.pdf?arnumber=8917626","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T09:38:19Z","timestamp":1643276299000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8917626\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2956550","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}