{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T21:19:21Z","timestamp":1776374361205,"version":"3.51.2"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T00:00:00Z","timestamp":1541030400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2018,11]]},"DOI":"10.1109\/tmi.2018.2837502","type":"journal-article","created":{"date-parts":[[2018,5,17]],"date-time":"2018-05-17T15:18:57Z","timestamp":1526570337000},"page":"2514-2525","source":"Crossref","is-referenced-by-count":1950,"title":["Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0752-9946","authenticated-orcid":false,"given":"Olivier","family":"Bernard","sequence":"first","affiliation":[{"name":"University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Lyon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-366X","authenticated-orcid":false,"given":"Alain","family":"Lalande","sequence":"additional","affiliation":[{"name":"Le2i Laboratory, CNRS FRE 2005, University of Burgundy, Dijon, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0713-9924","authenticated-orcid":false,"given":"Clement","family":"Zotti","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Sherbrooke, Sherbrooke, Canada"}]},{"given":"Frederick","family":"Cervenansky","sequence":"additional","affiliation":[{"name":"University of Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, University of Lyon 1, Lyon, France"}]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Pheng-Ann","family":"Heng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong"}]},{"given":"Irem","family":"Cetin","sequence":"additional","affiliation":[{"name":"Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain"}]},{"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[{"name":"Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain"}]},{"given":"Oscar","family":"Camara","sequence":"additional","affiliation":[{"name":"Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain"}]},{"given":"Miguel Angel","family":"Gonzalez Ballester","sequence":"additional","affiliation":[{"name":"Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain"}]},{"given":"Gerard","family":"Sanroma","sequence":"additional","affiliation":[{"name":"Barcelona Centre for New Medical Technologies, Universitat Pompeu Fabra, Barcelona, Spain"}]},{"given":"Sandy","family":"Napel","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA"}]},{"given":"Steffen","family":"Petersen","sequence":"additional","affiliation":[{"name":"William Harvey Research Institute, Queen Mary University of London, London, U.K."}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1802-1825","authenticated-orcid":false,"given":"Georgios","family":"Tziritas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Crete, Heraklion, Greece"}]},{"given":"Elias","family":"Grinias","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Crete, Heraklion, Greece"}]},{"given":"Mahendra","family":"Khened","sequence":"additional","affiliation":[{"name":"Department of Engineering Design, IIT Madras, Chennai, India"}]},{"given":"Varghese Alex","family":"Kollerathu","sequence":"additional","affiliation":[{"name":"Department of Engineering Design, IIT Madras, Chennai, India"}]},{"given":"Ganapathy","family":"Krishnamurthi","sequence":"additional","affiliation":[{"name":"Department of Engineering Design, IIT Madras, Chennai, India"}]},{"given":"Marc-Michel","family":"Roh\u00e9","sequence":"additional","affiliation":[{"name":"Inria-Asclepios Project, Sophia Antipolis, France"}]},{"given":"Xavier","family":"Pennec","sequence":"additional","affiliation":[{"name":"Inria-Asclepios Project, Sophia Antipolis, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6256-8350","authenticated-orcid":false,"given":"Maxime","family":"Sermesant","sequence":"additional","affiliation":[{"name":"Inria-Asclepios Project, Sophia Antipolis, France"}]},{"given":"Fabian","family":"Isensee","sequence":"additional","affiliation":[{"name":"Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany"}]},{"given":"Paul","family":"J\u00e4ger","sequence":"additional","affiliation":[{"name":"Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany"}]},{"given":"Klaus H.","family":"Maier-Hein","sequence":"additional","affiliation":[{"name":"Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany"}]},{"given":"Peter M.","family":"Full","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany"}]},{"given":"Ivo","family":"Wolf","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany"}]},{"given":"Sandy","family":"Engelhardt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3629-4384","authenticated-orcid":false,"given":"Christian F.","family":"Baumgartner","sequence":"additional","affiliation":[{"name":"Computer Vision Laboratory, ETH Z&#x00FC;rich, Z&#x00FC;rich, Switzerland"}]},{"given":"Lisa M.","family":"Koch","sequence":"additional","affiliation":[{"name":"Computer Vision and Geometry Group, ETH Z&#x00FC;rich, Z&#x00FC;rich, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5505-475X","authenticated-orcid":false,"given":"Jelmer M.","family":"Wolterink","sequence":"additional","affiliation":[{"name":"Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands"}]},{"given":"Ivana","family":"I\u0161gum","sequence":"additional","affiliation":[{"name":"Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands"}]},{"given":"Yeonggul","family":"Jang","sequence":"additional","affiliation":[{"name":"Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2416-8249","authenticated-orcid":false,"given":"Yoonmi","family":"Hong","sequence":"additional","affiliation":[{"name":"Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Seoul, South Korea"}]},{"given":"Jay","family":"Patravali","sequence":"additional","affiliation":[{"name":"Qure.ai company, Mumbai, India"}]},{"given":"Shubham","family":"Jain","sequence":"additional","affiliation":[{"name":"Qure.ai company, Mumbai, India"}]},{"given":"Olivier","family":"Humbert","sequence":"additional","affiliation":[{"name":"TIRO-UMR E 4320 Laboratory, University of Nice, Nice, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-5753","authenticated-orcid":false,"given":"Pierre-Marc","family":"Jodoin","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Sherbrooke, Sherbrooke, Canada"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-003-1957-x"},{"key":"ref38","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1161\/CIRCULATIONAHA.108.840827","article-title":"Diagnosis of arrhythmogenic right ventricular cardiomyopathy\/dysplasia","volume":"121","author":"marcus","year":"2010","journal-title":"Circulation"},{"key":"ref33","author":"poudel","year":"2016","journal-title":"Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation"},{"key":"ref32","first-page":"521","article-title":"Deep fusion net for multi-atlas segmentation: Application to cardiac MR images","author":"yang","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention&#x2014;MICCAI"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.009"},{"key":"ref30","author":"rupprecht","year":"2016","journal-title":"Deep active contours"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.3109\/10976649909080829"},{"key":"ref36","first-page":"125","article-title":"Deep learning trends for focal brain pathology segmentation in MRI","author":"havaei","year":"2015","journal-title":"Machine Learning for Health Informatics (Lecture Notes in Computer Science)"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref34","first-page":"246","article-title":"Regan, and D. Rueckert, &#x201C;Multi-input cardiac image super-resolution using convolutional neural networks","author":"oktay","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention&#x2014;MICCAI"},{"key":"ref28","first-page":"264","article-title":"Recognizing end-diastole and end-systole frames via deep temporal regression network","author":"kong","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention&#x2014;MICCAI"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2015.7318454"},{"key":"ref29","first-page":"138","article-title":"Automated quality assessment of cardiac mr images using convolutional neural networks","author":"zhang","year":"2016","journal-title":"Proc SASHIMI-MICCAI"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.eurheartj.a060113"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1161\/01.CIR.76.1.44","article-title":"Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction","volume":"76","author":"white","year":"1987","journal-title":"Circulation"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.12.004"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2218117"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2012.02.011"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.05.009"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2008.918327"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.09.005"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2002.804425"},{"key":"ref50","first-page":"101","article-title":"Automatic segmentation and disease classification using cardiac cine MR images","author":"wolterink","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref51","first-page":"152","article-title":"Class-balanced deep neural network for automatic ventricular structure segmentation","author":"yang","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref58","author":"bai","year":"2017","journal-title":"Human-level cmr image analysis with deep fully convolutional networks"},{"key":"ref57","first-page":"109","author":"bogaert","year":"2012","journal-title":"Cardiac function &#x201D; in Clinical Cardiac MRI"},{"key":"ref56","article-title":"SVF-net: Learning deformable image registration using shape matching","author":"roh\u00e9","year":"2017","journal-title":"Medical Image Computing and Computer-Assisted Intervention&#x2014;MICCAI"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950555"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref53","first-page":"82","article-title":"A radiomics approach to computer-aided diagnosis with cardiac cine-MRI","author":"cetin","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref52","first-page":"73","article-title":"GridNet with automatic shape prior registration for automatic MRI cardiac segmentation","author":"zotti","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.01.005"},{"key":"ref11","first-page":"490","article-title":"Cardiac left ventricle segmentation using convolutional neural network regression","author":"tan","year":"2016","journal-title":"Proc IECBES"},{"key":"ref40","first-page":"303","article-title":"A formula to estimate the approximate surface area if height and weight be known","volume":"5","author":"du bois","year":"1989","journal-title":"Nutrition"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.09.001"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.828354"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2010.04.013"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.01.014"},{"key":"ref16","author":"tran","year":"2017","journal-title":"A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI arXiv"},{"key":"ref17","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"long","year":"2014","journal-title":"Proc CVPR"},{"key":"ref18","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention&#x2014;MICCAI"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-016-0227-4"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23892"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1093\/eurheartj\/ehu284","article-title":"2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: The task force for the diagnosis and management of hypertrophic cardiomyopathy of the European society of cardiology (ESC)","volume":"35","author":"elliott","year":"2014","journal-title":"Eur Heart J"},{"key":"ref6","article-title":"Evaluation framework for algorithms segmenting short axis cardiac MRI","author":"radau","year":"2009","journal-title":"The MIDAS Journal - Cardiac MR Left Ventricle Segmentation Challenge"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.06.001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.10.004"},{"key":"ref7","first-page":"88","article-title":"Left ventricular segmentation challenge from cardiac MRI: A collation study","author":"suinesiaputra","year":"2011","journal-title":"Proc STACOM"},{"key":"ref49","first-page":"91","article-title":"Fast fully-automatic cardiac segmentation in MRI using MRF model optimization, substructures tracking and B-spline smoothing","author":"tziritas","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref9","year":"2015","journal-title":"The 2015 Kaggle Second Annual Data Science Bowl Accessed"},{"key":"ref46","first-page":"140","article-title":"Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest","author":"khened","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref45","first-page":"161","article-title":"Automatic segmentation of LV and RV in cardiac MRI","author":"jang","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref48","first-page":"170","article-title":"Automatic multi-atlas segmentation of myocardium with SVF-net","author":"rohe","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref47","first-page":"130","article-title":"2D&#x2013;3D fully convolutional neural networks for cardiac MR segmentation","author":"patravali","year":"2017","journal-title":"Proc STACOMMICCAI"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/237170.237244"},{"key":"ref41","first-page":"171","article-title":"Evaluation of cardiac structure segmentation in cine magnetic resonance imaging","author":"lalande","year":"2015","journal-title":"Multi-Modality Cardiac Imaging Processing and Analysis"},{"key":"ref44","first-page":"120","article-title":"Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features","author":"isensee","year":"2017","journal-title":"Proc MICCAI-STACOM"},{"key":"ref43","first-page":"111","article-title":"An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation","author":"baumgartner","year":"2017","journal-title":"Proc MICCAI-STACOM"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/8514078\/08360453.pdf?arnumber=8360453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:50:10Z","timestamp":1774554610000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8360453\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11]]},"references-count":58,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2018.2837502","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11]]}}}