{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T05:51:40Z","timestamp":1775886700078,"version":"3.50.1"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001826","name":"ZonMw","doi-asserted-by":"publisher","award":["104003009"],"award-info":[{"award-number":["104003009"]}],"id":[{"id":"10.13039\/501100001826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1109\/tmi.2018.2883807","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T20:13:05Z","timestamp":1543435985000},"page":"1588-1598","source":"Crossref","is-referenced-by-count":259,"title":["A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography"],"prefix":"10.1109","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6739-3297","authenticated-orcid":false,"given":"Majd","family":"Zreik","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6084-0656","authenticated-orcid":false,"given":"Robbert W.","family":"van Hamersvelt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5505-475X","authenticated-orcid":false,"given":"Jelmer M.","family":"Wolterink","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1885-5499","authenticated-orcid":false,"given":"Tim","family":"Leiner","sequence":"additional","affiliation":[]},{"given":"Max A.","family":"Viergever","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-5034","authenticated-orcid":false,"given":"Ivana","family":"Isgum","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref38","author":"kingma","year":"2014","journal-title":"Adam A method for stochastic optimization"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/978-3-319-46976-8_15","article-title":"Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data","author":"andermatt","year":"2016","journal-title":"Deep Learning and Data Labeling for Medical Applications"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref31","first-page":"83","article-title":"Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation","author":"poudel","year":"2016","journal-title":"Reconstruction Segmentation and Analysis of Medical Images"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2014.09.005"},{"key":"ref37","first-page":"315","article-title":"Deep sparse rectifier neural networks","author":"glorot","year":"2011","journal-title":"Proc 14th Int Conf Artif Intell Statist"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref35","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proceedings of the 32nd Intl Conf on Machine Learning"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.09.005"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2010.10.011"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2203010055"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.04.004"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2769839"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363515"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1136\/hrt.2009.184226"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.10100681"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15948-0_1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-010-0494-8"},{"key":"ref18","first-page":"380","article-title":"HALE: Healthy area of lumen estimation for vessel stenosis quantification","author":"sankaran","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2010.10.014"},{"key":"ref28","first-page":"4694","article-title":"Beyond short snippets: Deep networks for video classification","author":"ng","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2005.10.065"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1118\/1.4945696"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.4065\/mcp.2009.0391"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298958"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10554-007-9281-1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2008.07.031"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.jcct.2016.04.005","article-title":"CAD-RADSTM coronary artery disease&#x2014;Reporting and data system. An expert consensus document of the society of cardiovascular computed tomography (SCCT), the American college of radiology (ACR) and the North American society for cardiovascular imaging (NASCI). Endorsed by the American college of cardiology","volume":"10","author":"cury","year":"2016","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcct.2009.01.001"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.05.007"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"38e","DOI":"10.1161\/CIR.0000000000000350","article-title":"Heart disease and stroke statistics&#x2013;2016 update","volume":"133","author":"mozaffarian","year":"2016","journal-title":"Circulation"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.03.004"},{"key":"ref22","article-title":"Vessel segmentation using implicit model-guided level sets","author":"wang","year":"2012","journal-title":"Proc MICCAI Workshop 3D Cardiovascular Imag MICCAI Segmentation Challenge"},{"key":"ref21","article-title":"Automatic stenoses detection, quantification and lumen segmentation of the coronary arteries using a two point centerline extraction scheme","author":"shahzad","year":"2012","journal-title":"Proc MICCAI Workshop 3D Cardiovascular Imag MICCAI Segmentation Challenge"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref41","first-page":"424","article-title":"3D U-net: Learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7eke","year":"2016","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref23","article-title":"Frenchcoast: Fast, robust extraction for the nice challenge on coronary artery segmentation of the tree","author":"broersen","year":"2012","journal-title":"Proc MICCAI Workshop 3D Cardiovascular Imag MICCAI Segmentation Challenge"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298878"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.826946"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.10.005"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/8750971\/08550784.pdf?arnumber=8550784","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:51:20Z","timestamp":1657745480000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8550784\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":43,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2018.2883807","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7]]}}}