{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:38:13Z","timestamp":1777286293440,"version":"3.51.4"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61971118, No.61373088, No.61402298"],"award-info":[{"award-number":["No.61971118, No.61373088, No.61402298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of Liaoning","award":["No. LJKMZ20220523"],"award-info":[{"award-number":["No. LJKMZ20220523"]}]},{"name":"Aviation Science Foundation","award":["2019ZE054009"],"award-info":[{"award-number":["2019ZE054009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11517-025-03512-w","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:23:09Z","timestamp":1770682989000},"page":"1281-1298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiac multi-structure segmentation network based on the fused dual attention mechanism"],"prefix":"10.1007","volume":"64","author":[{"given":"Guodong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luchang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenwen","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronghui","family":"Ju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxuan","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0130-7401","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"issue":"25","key":"3512_CR1","doi-asserted-by":"publisher","first-page":"2350","DOI":"10.1016\/j.jacc.2023.11.007","volume":"82","author":"GA Mensah","year":"2023","unstructured":"Mensah GA, Fuster V, Murray CJ, Roth GA, Cardiovascular Diseases GB, Collaborators R (2023) Global burden of cardiovascular diseases and risks, 1990\u20132022. J Am Coll Cardiol 82(25):2350\u20132473","journal-title":"J Am Coll Cardiol"},{"issue":"9","key":"3512_CR2","doi-asserted-by":"publisher","first-page":"3912","DOI":"10.3390\/app11093912","volume":"11","author":"M Habijan","year":"2021","unstructured":"Habijan M, Gali\u0107 I, Leventi\u0107 H, Romi\u0107 K (2021) Whole heart segmentation using 3D fm-pre-resnet encoder-decoder based architecture with variational autoencoder regularization. Appl Sci 11(9):3912","journal-title":"Appl Sci"},{"key":"3512_CR3","doi-asserted-by":"crossref","unstructured":"Kawel-Boehm N, Hetzel SJ, Ambale-Venkatesh B, Captur G, Francois CJ, Jerosch-Herold M, Salerno M, Teague SD, Valsangiacomo-Buechel E, Geest RJ et al (2020) Reference ranges (\u201cnormal values\u201d) for cardiovascular magnetic resonance (CMR) in adults and children: 2020 update. J Cardiovasc Magn Reson 22(1):1\u201363","DOI":"10.1186\/s12968-020-00683-3"},{"issue":"7","key":"3512_CR4","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1161\/CIRCULATIONAHA.122.059280","volume":"146","author":"S Rosch","year":"2022","unstructured":"Rosch S, Kresoja K-P, Besler C, Fengler K, Sch\u00f6ber AR, Roeder M, L\u00fccke C, Gutberlet M, Klingel K, Thiele H et al (2022) Characteristics of heart failure with preserved ejection fraction across the range of left ventricular ejection fraction. Circulation 146(7):506\u2013518","journal-title":"Circulation"},{"issue":"2","key":"3512_CR5","doi-asserted-by":"publisher","first-page":"127","DOI":"10.5152\/AnatolJCardiol.2021.367","volume":"26","author":"K Esenbo\u011fa","year":"2022","unstructured":"Esenbo\u011fa K, K\u0131l\u0131\u00e7kap M, Peker E, Kozluca V, Koca \u00c7, Kaya CT, Uluda\u011f DMG, Din\u00e7er \u0130 (2022) Agreement between visually estimated left ventricular ejection fraction on echocardiography and quantitative measurements using cardiac magnetic resonance. Anatol J Cardiol 26(2):127","journal-title":"Anatol J Cardiol"},{"key":"3512_CR6","doi-asserted-by":"crossref","unstructured":"Sundgaard JV, Juhl KA, Kofoed KF, Paulsen RR (2020) Multi-planar whole heart segmentation of 3D CT images using 2D spatial propagation CNN. In: Medical imaging 2020: image processing, vol 11313. SPIE, pp 477\u2013484","DOI":"10.1117\/12.2548015"},{"key":"3512_CR7","doi-asserted-by":"crossref","unstructured":"Dormer JD, Ma L, Halicek M, Reilly CM, Schreibmann E, Fei B (2018) Heart chamber segmentation from CT using convolutional neural networks. In: Medical imaging 2018: biomedical applications in molecular, structural, and functional imaging, vol 10578. SPIE, pp 659\u2013664","DOI":"10.1117\/12.2293554"},{"key":"3512_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.media.2018.10.004","volume":"51","author":"M Khened","year":"2019","unstructured":"Khened M, Kollerathu VA, Krishnamurthi G (2019) Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers. Med Image Anal 51:21\u201345","journal-title":"Med Image Anal"},{"key":"3512_CR9","doi-asserted-by":"crossref","unstructured":"Yang X, Tian, X (2022) TransnUNet: using attention mechanism for whole heart segmentation. In: 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, pp 553\u2013556","DOI":"10.1109\/ICPECA53709.2022.9719101"},{"key":"3512_CR10","doi-asserted-by":"crossref","unstructured":"Habijan M, Leventi\u0107 H, Gali\u0107 I, Babin D (2019) Whole heart segmentation from CT images using 3D U-net architecture. In: 2019 international conference on systems, signals and image processing (IWSSIP). IEEE, pp 121\u2013126","DOI":"10.1109\/IWSSIP.2019.8787253"},{"key":"3512_CR11","doi-asserted-by":"publisher","first-page":"105191","DOI":"10.1016\/j.compbiomed.2021.105191","volume":"142","author":"S Bruns","year":"2022","unstructured":"Bruns S, Wolterink JM, Boogert TP, Runge JH, Bouma BJ, Henriques JP, Baan J, Viergever MA, Planken RN, I\u0161gum I (2022) Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT. Comput Biol Med 142:105191","journal-title":"Comput Biol Med"},{"issue":"10","key":"3512_CR12","doi-asserted-by":"publisher","first-page":"105008","DOI":"10.1088\/1361-6560\/ac692d","volume":"67","author":"S Momin","year":"2022","unstructured":"Momin S, Lei Y, McCall NS, Zhang J, Roper J, Harms J, Tian S, Lloyd MS, Liu T, Bradley JD et al (2022) Mutual enhancing learning-based automatic segmentation of CT cardiac substructure. Phys Med Biol 67(10):105008","journal-title":"Phys Med Biol"},{"issue":"3","key":"3512_CR13","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","volume":"8","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Xu T-X, Liu J-J, Liu Z-N, Jiang P-T, Mu T-J, Zhang S-H, Martin RR, Cheng M-M, Hu S-M (2022) Attention mechanisms in computer vision: a survey. Comput Visual Media 8(3):331\u2013368","journal-title":"Comput Visual Media"},{"issue":"5","key":"3512_CR14","doi-asserted-by":"publisher","first-page":"5207","DOI":"10.3934\/mbe.2022244","volume":"19","author":"Z Fu","year":"2022","unstructured":"Fu Z, Zhang J, Luo R, Sun Y, Deng D, Xia L (2022) TF-Unet: an automatic cardiac MRI image segmentation method. Math Biosci Eng 19(5):5207\u20135222","journal-title":"Math Biosci Eng"},{"key":"3512_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox, T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"11","key":"3512_CR16","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P-A, Cetin I, Lekadir K, Camara O, Ballester MAG et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"3512_CR17","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.3390\/life12101570","volume":"12","author":"L Zhao","year":"2022","unstructured":"Zhao L, Zhou D, Jin X, Zhu W (2022) Nn-TransUNet: an automatic deep learning pipeline for heart MRI segmentation. Life 12(10):1570","journal-title":"Life"},{"key":"3512_CR18","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y Transunet: transformers make strong encoders for medical image segmentation"},{"key":"3512_CR19","doi-asserted-by":"crossref","unstructured":"Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G, Norajitra T, Wirkert S et al (2019) nnU-Net: Self-adapting framework for U-Net-based medical image segmentation. In: Bildverarbeitung F\u00fcr die Medizin 2019: Algorithmen\u2013Systeme\u2013Anwendungen. Proceedings des Workshops Vom 17. Bis 19. M\u00e4rz 2019 in L\u00fcbeck. Springer, pp 22\u201322","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"3512_CR20","doi-asserted-by":"crossref","unstructured":"Zheng Y, Wu Z, Ji F, Du L, Yang Z (2025) DF-TransUNet: a novel TransUNet model of pixel level classification for cardiac MR image segmentation. Magnetic Resonance Imaging, 110502","DOI":"10.1016\/j.mri.2025.110502"},{"key":"3512_CR21","doi-asserted-by":"publisher","first-page":"102470","DOI":"10.1016\/j.compmedimag.2024.102470","volume":"118","author":"M Imran","year":"2024","unstructured":"Imran M, Krebs JR, Gopu VRR, Fazzone B, Sivaraman VB, Kumar A, Viscardi C, Heithaus RE, Shickel B, Zhou Y et al (2024) CIS-UNet: multi-class segmentation of the aorta in computed tomography angiography via context-aware shifted window self-attention. Comput Med Imaging Graph 118:102470","journal-title":"Comput Med Imaging Graph"},{"key":"3512_CR22","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1007\/s13239-020-00494-8","volume":"11","author":"M Habijan","year":"2020","unstructured":"Habijan M, Babin D, Gali\u0107 I, Leventi\u0107 H, Romi\u0107 K, Velicki L, Pi\u017eurica A (2020) Overview of the whole heart and heart chamber segmentation methods. Cardiovasc Eng Technol 11:725\u2013747","journal-title":"Cardiovasc Eng Technol"},{"key":"3512_CR23","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2016: 19th international conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19. Springer, pp 424\u2013432","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"3512_CR24","doi-asserted-by":"crossref","unstructured":"Tong Q, Ning M, Si W, Liao X, Qin, J (2018) 3D deeply-supervised u-net based whole heart 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, Quebec City, Canada, September 10-14, 2017, Revised Selected Papers 8. Springer, pp 224\u2013232","DOI":"10.1007\/978-3-319-75541-0_24"},{"key":"3512_CR25","doi-asserted-by":"crossref","unstructured":"Payer C, \u0160tern D, Bischof H, Urschler M (2017) Multi-label whole heart segmentation using CNNs and anatomical label configurations. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 190\u2013198","DOI":"10.1007\/978-3-319-75541-0_20"},{"key":"3512_CR26","unstructured":"Xu Z, Wu Z, Feng J (2018) CFUN: combining faster R-CNN and U-net network for efficient whole heart segmentation. arXiv:1812.04914"},{"key":"3512_CR27","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Advances in neural information processing systems 28"},{"key":"3512_CR28","doi-asserted-by":"crossref","unstructured":"Kanakatte A, Bhatia D, Ghose, A (2021) Heart region segmentation using dense VNet from multimodality images. In: 2021 43rd annual international conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, pp 3255\u20133258","DOI":"10.1109\/EMBC46164.2021.9630303"},{"key":"3512_CR29","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"3512_CR30","doi-asserted-by":"crossref","unstructured":"Bruns S, Wolterink JM, Van Den Boogert TP, Henriques JP, Baan J, Planken RN, I\u0161gum I (2021) Automatic whole-heart segmentation in 4D TAVI treatment planning CT. In: Medical imaging 2021: image processing, vol 11596. SPIE, pp 55\u201362","DOI":"10.1117\/12.2581020"},{"key":"3512_CR31","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","volume":"452","author":"Z Niu","year":"2021","unstructured":"Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48\u201362","journal-title":"Neurocomputing"},{"key":"3512_CR32","unstructured":"Mnih V, Heess N, Graves A et al (2014) Recurrent models of visual attention. Advances in neural information processing systems 27"},{"key":"3512_CR33","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun, G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3512_CR34","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV). pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"3512_CR35","doi-asserted-by":"crossref","unstructured":"Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp 13713\u201313722","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"3512_CR36","unstructured":"Park J, Woo S, Lee J-Y, Kweon IS (2018) BAM: bottleneck attention module. arXiv:1807.06514"},{"key":"3512_CR37","doi-asserted-by":"crossref","unstructured":"Xue H, Liu C, Wan F, Jiao J, Ji X, Ye, Q (2019) DANet: divergent activation for weakly supervised object localization. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 6589\u20136598","DOI":"10.1109\/ICCV.2019.00669"},{"key":"3512_CR38","doi-asserted-by":"crossref","unstructured":"Zhang Q-L, Yang Y-B (2021) SA-Net: shuffle attention for deep convolutional neural networks. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 2235\u20132239","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"3512_CR39","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B et al (2018) Attention U-Net: learning where to look for the pancreas. arXiv:1804.03999"},{"issue":"12","key":"3512_CR40","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2018","unstructured":"Zhuang X (2018) Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans Pattern Anal Mach Intell 41(12):2933\u20132946","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3512_CR41","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang X, Shen J (2016) Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med Image Anal 31:77\u201387","journal-title":"Med Image Anal"},{"key":"3512_CR42","doi-asserted-by":"crossref","unstructured":"Luo X, Zhuang X (2022) X-metric: an N-dimensional information-theoretic framework for groupwise registration and deep combined computing. IEEE Transactions on Pattern Analysis and Machine Intelligence","DOI":"10.1109\/TPAMI.2022.3225418"},{"key":"3512_CR43","doi-asserted-by":"crossref","unstructured":"Zheng H, Yang L, Han J, Zhang Y, Liang P, Zhao Z, Wang C, Chen DZ (2019) HFA-Net: 3D cardiovascular image segmentation with asymmetrical pooling and content-aware fusion. In: Medical image computing and computer assisted intervention\u2013MICCAI 2019: 22nd international conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part II 22. Springer, pp 759\u2013767","DOI":"10.1007\/978-3-030-32245-8_84"},{"key":"3512_CR44","doi-asserted-by":"crossref","unstructured":"Zhang Y, Gu P, Zhang Y, Wang C, Chen DZ (2023) GrNT: gate-regularized network training for improving multi-scale fusion in medical image segmentation. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, pp 1\u20135","DOI":"10.1109\/ISBI53787.2023.10230431"},{"key":"3512_CR45","unstructured":"Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, Murrey B, Myronenko A, Zhao C, Yang D et al (2022) MONAI: an open-source framework for deep learning in healthcare. arXiv:2211.02701"},{"key":"3512_CR46","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D (2022) UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision. pp 574\u2013584","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"3512_CR47","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D (2021) Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: International MICCAI Brainlesion workshop. Springer, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"3512_CR48","doi-asserted-by":"crossref","unstructured":"Liu J, Zhang Y, Chen J-N, Xiao J, Lu Y, A Landman B, Yuan Y, Yuille A, Tang Y, Zhou Z (2023) Clip-driven universal model for organ segmentation and tumor detection. In: Proceedings of the IEEE\/CVF international conference on computer vision. pp 21152\u201321164","DOI":"10.1109\/ICCV51070.2023.01934"},{"issue":"5","key":"3512_CR49","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual U-Net. IEEE Geosci Remote Sens Lett 15(5):749\u2013753","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"3512_CR50","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116\u20131128","journal-title":"Neuroimage"},{"key":"3512_CR51","doi-asserted-by":"crossref","unstructured":"Chen H, Qi X, Cheng J, Heng P (2016) Deep contextual networks for neuronal structure segmentation. In: Proceedings of the AAAI conference on artificial intelligence, vol 30","DOI":"10.1609\/aaai.v30i1.10141"},{"key":"3512_CR52","doi-asserted-by":"crossref","unstructured":"Yu L, Cheng J-Z, Dou Q, Yang X, Chen H, Qin J, Heng P-A (2017) Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. In: Medical image computing and computer-assisted intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II 20. Springer, pp 287\u2013295","DOI":"10.1007\/978-3-319-66185-8_33"},{"key":"3512_CR53","unstructured":"Kosaraju A, Goyal A, Grigorova Y, Makaryus AN (2017) Left ventricular ejection fraction"},{"issue":"8","key":"3512_CR54","doi-asserted-by":"publisher","first-page":"2170","DOI":"10.1109\/TMI.2021.3073381","volume":"40","author":"Q Lyu","year":"2021","unstructured":"Lyu Q, Shan H, Xie Y, Kwan AC, Otaki Y, Kuronuma K, Li D, Wang G (2021) Cine cardiac MRI motion artifact reduction using a recurrent neural network. IEEE Trans Med Imaging 40(8):2170\u20132181","journal-title":"IEEE Trans Med Imaging"},{"key":"3512_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42444-020-00020-w","volume":"21","author":"O-S Kwon","year":"2020","unstructured":"Kwon O-S, Lee J, Lim S, Park J-W, Han H-J, Yang S-H, Hwang I, Yu HT, Kim T-H, Uhm J-S et al (2020) Accuracy and clinical feasibility of 3D-myocardial thickness map measured by cardiac computed tomogram. Int J Arrhythmia 21:1\u201311","journal-title":"Int J Arrhythmia"},{"issue":"5","key":"3512_CR56","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1111\/j.1475-097X.2004.00569.x","volume":"24","author":"H Engblom","year":"2004","unstructured":"Engblom H, Hedstr\u00f6m E, Palmer J, Wagner GS, Arheden H (2004) Determination of the left ventricular long-axis orientation from a single short-axis MR image: relation to BMI and age. Clin Physiol Funct Imaging 24(5):310\u2013315","journal-title":"Clin Physiol Funct Imaging"},{"issue":"12","key":"3512_CR57","first-page":"2457","volume":"14","author":"S Gupta","year":"2021","unstructured":"Gupta S, Ge Y, Singh A, Gr\u00e4ni C, Kwong RY (2021) Multimodality imaging assessment of myocardial fibrosis. Cardiovasc Imaging 14(12):2457\u20132469","journal-title":"Cardiovasc Imaging"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03512-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03512-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03512-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:49:19Z","timestamp":1777283359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03512-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,10]]},"references-count":57,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["3512"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03512-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,10]]},"assertion":[{"value":"25 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}