{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T12:14:36Z","timestamp":1775564076801,"version":"3.50.1"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T00:00:00Z","timestamp":1772841600000},"content-version":"vor","delay-in-days":65,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.03.091","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:39:40Z","timestamp":1774355980000},"page":"1120-1127","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A comparative study of CNN and FCN performance for short-axis left ventricle segmentation in cardiac cine MR sequences"],"prefix":"10.1016","volume":"278","author":[{"given":"Sameh","family":"Oueslati","sequence":"first","affiliation":[]},{"given":"Basel","family":"Solaiman","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.03.091_bib1","series-title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: Is the problem solved?","author":"Bernard","year":"2018"},{"key":"10.1016\/j.procs.2026.03.091_bib2","series-title":"Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation, in Reconstruction, Segmentation, and Analysis of Medical Images, pp.83\u201394","author":"Poudel","year":"2016"},{"key":"10.1016\/j.procs.2026.03.091_bib3","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","article-title":"Multi-scale patch and multi-modality atlases for whole heart segmentation of mri","volume":"31","author":"Zhuang","year":"2016","journal-title":"Medical image analysis"},{"key":"10.1016\/j.procs.2026.03.091_bib4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.media.2017.04.002","article-title":"Convolutional neural network regression for short-axis left ventricle segmentation in cardiac cine mri sequences","volume":"39","author":"Tan","year":"2017","journal-title":"Medical image analysis"},{"key":"10.1016\/j.procs.2026.03.091_bib5","doi-asserted-by":"crossref","unstructured":"Bin Kong, Yiqiang Zhan, Min Shin, Thomas Denny, and Shaoting Zhang (2016). Recognizing end-diastole and endsystole frames via deep temporal regression network, in International conference on medical image computing and computer-assisted intervention. Springer,pp. 264\u2013272.","DOI":"10.1007\/978-3-319-46726-9_31"},{"key":"10.1016\/j.procs.2026.03.091_bib6","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.media.2016.05.009","article-title":"Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance","author":"Ngo","year":"2017","journal-title":"Medical image analysis, vol.35"},{"key":"10.1016\/j.procs.2026.03.091_bib7","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.media.2016.01.005","article-title":"A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac mri","volume":"30","author":"Avendi","year":"2016","journal-title":"Medical image analysis"},{"key":"10.1016\/j.procs.2026.03.091_bib8","doi-asserted-by":"crossref","unstructured":"Dong Yang, Qiaoying Huang, Leon Axel, and Dimitris Metaxas, (2018). Multi-component deformable models coupled with 2d-3d u-net for automated probabilistic segmentation of cardiac walls and blood, in Biomedical Imaging (ISBI 2018) IEEE. pp. 479\u2013483.","DOI":"10.1109\/ISBI.2018.8363620"},{"issue":"4","key":"10.1016\/j.procs.2026.03.091_bib9","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1109\/JBHI.2014.2370952","article-title":"Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging","volume":"19","author":"Suinesiaputra","year":"2015","journal-title":"Ieee J. Biomed Health."},{"key":"10.1016\/j.procs.2026.03.091_bib10","doi-asserted-by":"crossref","unstructured":"Samhitha, D. S. & Ashwini K. (2024). Deep Learning based Terrain Classification of Mars Raw Images using UNet and FCN Models. 11th International Conference on Computing for Sustainable Global Development. (INDIACom), New Delhi, India, pp. 855-860","DOI":"10.23919\/INDIACom61295.2024.10498424"},{"key":"10.1016\/j.procs.2026.03.091_bib11","doi-asserted-by":"crossref","unstructured":"Neelam, B. Kumar, Palakayala. Mbangweta, P., K. Raparla K. & Devi, S. A. (2023). FCN Based Deep Learning Architecture for Medical Image Segmentation. 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India, pp. 556-562.","DOI":"10.1109\/ICECAA58104.2023.10212108"},{"key":"10.1016\/j.procs.2026.03.091_bib12","unstructured":"Tran PV. (2019). A fully convolutional neural network for cardiac segmentation in short-axis MRI, arxiv (2016) abs\/1604.00494."},{"issue":"51","key":"10.1016\/j.procs.2026.03.091_bib13","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","article-title":"Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers","volume":"2019","author":"Khened","year":"2019","journal-title":"Med Image Anal."},{"key":"10.1016\/j.procs.2026.03.091_bib14","doi-asserted-by":"crossref","unstructured":"Shelhamer E, Long J, Darrell T. (2017). Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell.39:640\u201351.","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"10.1016\/j.procs.2026.03.091_bib15","doi-asserted-by":"crossref","unstructured":"Jang Y, Hong Y, Ha S, Kim S, Chang HJ. (2017). Automatic segmentation of LV and RV in cardiac MRI, In: Pop M, Sermesant M, Jodoin P-M, Lalande A, Zhuang X, Yang G, Young AA, Bernard O, editors. International Workshop on Statistical Atlases and Computational Models of the Heart. Springer. p. 161\u20139.","DOI":"10.1007\/978-3-319-75541-0_17"},{"key":"10.1016\/j.procs.2026.03.091_bib16","doi-asserted-by":"crossref","unstructured":"Yang X, Bian C, Yu L, Ni D, Heng PA. (2017). Class-balanced deep neural network for automatic ventricular structure segmentation, In: Pop M, Sermesant M, Jodoin P-M, Lalande A, Zhuang X, Yang G, Young AA, Bernard O, Proceedings of the 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. Quebec City, QC: Springer International Publishing. p. 152\u201360.","DOI":"10.1007\/978-3-319-75541-0_16"},{"key":"10.1016\/j.procs.2026.03.091_bib17","doi-asserted-by":"crossref","unstructured":"Fahmy AS, El-Rewaidy H, Nezafat M, Nakamori S, Nezafat R. (2019). Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks, J Cardiovasc Magn Reson. 21:1\u201312. 10.","DOI":"10.1186\/s12968-018-0516-1"},{"key":"10.1016\/j.procs.2026.03.091_bib18","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1109\/JBHI.2018.2865450","article-title":"Convolutional neural Network with shape prior applied to cardiac MRI segmentation","volume":"23","author":"Zotti","year":"2019","journal-title":"IEEE J Biomed Health Inform."},{"key":"10.1016\/j.procs.2026.03.091_bib19","doi-asserted-by":"crossref","unstructured":"Zotti C, Luo Z, Lalande A, Humbert O, Jodoin PM. (2017). GridNet with automatic shape prior registration for automatic MRI cardiac segmentation, Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. Vol. 10663.QC: Springer; p. 73\u201381.","DOI":"10.1007\/978-3-319-75541-0_8"},{"key":"10.1016\/j.procs.2026.03.091_bib20","doi-asserted-by":"crossref","first-page":"3499","DOI":"10.1109\/TBME.2019.2906667","article-title":"Dilated-inception net: multi-scale feature aggregation for cardiac right ventricle segmentation","volume":"66","author":"Li","year":"2019","journal-title":"IEEE Trans Biomed Eng."},{"key":"10.1016\/j.procs.2026.03.091_bib21","doi-asserted-by":"crossref","first-page":"1463","DOI":"10.1049\/joe.2018.8302","article-title":"Invert-U-Net DNN segmentation model for MRI cardiac left ventricle segmentation","author":"Cong","year":"2018","journal-title":"J Eng."},{"key":"10.1016\/j.procs.2026.03.091_bib22","doi-asserted-by":"crossref","unstructured":"Chen M, Fang L, Liu H. FR-NET (2019). Focal Loss constrained deep residual networks for segmentation of cardiac MRI, In: 16th IEEE International Symposium on Biomedical Imaging, ISBI, 2019. Venice: IEEE; p. 764\u20137.","DOI":"10.1109\/ISBI.2019.8759556"},{"key":"10.1016\/j.procs.2026.03.091_bib23","doi-asserted-by":"crossref","unstructured":"Chen C, Biffi C, Tarroni G, Petersen S, Bai W, Rueckert D. (2019) \u201cLearning shape priors for robust cardiac MR segmentation from multi-view images,\u201d In: Medical Image Computing and Computer Assisted Intervention. p. 523\u201331.","DOI":"10.1007\/978-3-030-32245-8_58"},{"key":"10.1016\/j.procs.2026.03.091_bib24","doi-asserted-by":"crossref","unstructured":"Sander J, de Vos BD, Wolterink JM, I\u0161gum I. (2019). Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI. In: Medical Imaging 2019: Image Processing. Vol. 10949. International Society for Optics and Photonics p. 73\u201381.","DOI":"10.1117\/12.2511699"},{"key":"10.1016\/j.procs.2026.03.091_bib25","first-page":"1026","article-title":"Delving deep into rectifiers: surpassing human-level performance on image net classification","author":"He","year":"2015","journal-title":"In: Proceedings of the IEEE International Conference on Computer Vision"},{"key":"10.1016\/j.procs.2026.03.091_bib26","series-title":"Automatic differentiation in pytorch. In: NIPS2017 Autodiff Workshop: The Future Machine LearningSoftware and Techniques,\u201d Long Beach","author":"Paszke","year":"2017"},{"key":"10.1016\/j.procs.2026.03.091_bib27","series-title":"Review of Automatic Segmentation of MRI Based Brain Tumour Using U-Net Architecture. In 2020 Fourth International Conference on Inventive Systems and Control (ICISC) (pp. 46-50)","author":"Mathews","year":"2020"},{"key":"10.1016\/j.procs.2026.03.091_bib28","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural networks"},{"key":"10.1016\/j.procs.2026.03.091_bib29","doi-asserted-by":"crossref","first-page":"104699","DOI":"10.1016\/j.compbiomed.2021.104699","article-title":"Sharp U-Net: depth wise convolutional network for biomedical image segmentation","volume":"136","author":"Zunair","year":"2021","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.procs.2026.03.091_bib30","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","article-title":"Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers","volume":"51","author":"Khened","year":"2019","journal-title":"Medical image analysis"},{"key":"10.1016\/j.procs.2026.03.091_bib31","doi-asserted-by":"crossref","unstructured":"Zotti, C., Luo, Z., Lalande, A., et al (2018). Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE journal of biomedical and health informatics, 23(3), 1119- 1128","DOI":"10.1109\/JBHI.2018.2865450"},{"key":"10.1016\/j.procs.2026.03.091_bib32","series-title":"Fast fully-automatic cardiac segmentation in MRI using MRF model optimization, substructures tracking and B-spline smoothing. 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10-14, 2017","author":"Grinias","year":"2018"},{"key":"10.1016\/j.procs.2026.03.091_bib33","series-title":"2D-3D fully convolutional neural networks for cardiac MR segmentation. In Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017","author":"Patravali","year":"2018"},{"key":"10.1016\/j.procs.2026.03.091_bib34","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neunet.2020.03.007","article-title":"AdaEn-Net: An ensemble of adaptive 2D\u20133D Fully Convolutional Networks for medical image segmentation","volume":"126","author":"Calisto","year":"2020","journal-title":"Neural Networks"},{"key":"10.1016\/j.procs.2026.03.091_bib35","series-title":"Fully automatic segmentation of short-axis cardiac MRI using modified deep layer aggregation. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 793-797)","author":"Li","year":"2019"},{"key":"10.1016\/j.procs.2026.03.091_bib36","doi-asserted-by":"crossref","first-page":"103877","DOI":"10.1016\/j.compbiomed.2020.103877","article-title":"Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach\u201d","volume":"122","author":"Xie","year":"2020","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.procs.2026.03.091_bib37","series-title":"Rethinking the inception architecture for computer vision","first-page":"2818","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.procs.2026.03.091_bib38","series-title":"Estimation of the volume of the left ventricle from MRI images using deep neural networks","author":"Liao","year":"2019"}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006861?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006861?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:33:43Z","timestamp":1775561623000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926006861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":38,"alternative-id":["S1877050926006861"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.091","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A comparative study of CNN and FCN performance for short-axis left ventricle segmentation in cardiac cine MR sequences","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.091","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}