{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T14:03:28Z","timestamp":1756994608657,"version":"3.37.3"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"beijing natural science foundation","award":["5182018"],"award-info":[{"award-number":["5182018"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11042-022-14202-2","type":"journal-article","created":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T14:06:40Z","timestamp":1670335600000},"page":"20951-20973","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lossless segmentation of cardiac medical images by a resolution consistent network with nondamage data preprocessing"],"prefix":"10.1007","volume":"82","author":[{"given":"Yifan","family":"Yan","sequence":"first","affiliation":[]},{"given":"Chenglizhao","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1270-6257","authenticated-orcid":false,"given":"Jingyang","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"14202_CR1","doi-asserted-by":"crossref","unstructured":"Ang Y, Hong Y, Ha S et al (2017) Automatic segmentation of LV and RV in cardiac MRI. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 161\u2013169","DOI":"10.1007\/978-3-319-75541-0_17"},{"key":"14202_CR2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14202_CR3","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 et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging 37:2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"key":"14202_CR4","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.media.2018.08.003","volume":"49","author":"M Bustamante","year":"2018","unstructured":"Bustamante M, Gupta V, Forsberg D et al (2018) Automated multi-atlas segmentation of cardiac 4D flow MRI. Med Image Anal 49:128\u2013140","journal-title":"Med Image Anal"},{"key":"14202_CR5","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.neunet.2020.03.007","volume":"126","author":"MB Calisto","year":"2020","unstructured":"Calisto MB, Lai-Yuen SK (2020) Adaen-net: An ensemble of adaptive 2D\u20133D Fully Convolutional Networks for medical image segmentation. Neural Netw 126:76\u201394","journal-title":"Neural Netw"},{"key":"14202_CR6","doi-asserted-by":"crossref","unstructured":"Chaurasia A, Culurciello E (2017) Linknet: Exploiting encoder representations for efficient semantic segmentation. In: 2017 IEEE visual communications and image processing (VCIP). IEEE, pp 1\u20134","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"14202_CR7","doi-asserted-by":"publisher","unstructured":"Chen K, Franko K, Sang R (2021) Structured model pruning of convolutional networks on tensor processing units. https:\/\/doi.org\/10.48550\/arXiv.2107.04191. arXiv:2107.04191","DOI":"10.48550\/arXiv.2107.04191"},{"key":"14202_CR8","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv:1412.7062"},{"key":"14202_CR9","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"LC Chen","year":"2017","unstructured":"Chen LC, Papandreou G, Kokkinos I et al (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"14202_CR10","unstructured":"Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"key":"14202_CR11","doi-asserted-by":"crossref","unstructured":"Chen C, Wang G, Peng C, Zhang D, Fang Y, Qin H (2020) Exploring rich and efficient spatial temporal interactions for real time video salient object detection. arXiv:2008.02973","DOI":"10.1109\/TIP.2021.3068644"},{"key":"14202_CR12","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1109\/TIP.2019.2934350","volume":"29","author":"C Chen","year":"2019","unstructured":"Chen C, Wang G, Peng C, Zhang X, Qin H (2019) Improved robust video saliency detection based on long-term spatial-temporal information. IEEE Trans Image Process 29:1090\u20131100","journal-title":"IEEE Trans Image Process"},{"key":"14202_CR13","doi-asserted-by":"publisher","first-page":"4296","DOI":"10.1109\/TIP.2020.2968250","volume":"29","author":"C Chen","year":"2020","unstructured":"Chen C, Wei J, Peng C, Zhang W, Qin H (2020) Improved saliency detection in RGB-d images using two-phase depth estimation and selective deep fusion. IEEE Trans Image Process 29:4296\u20134307","journal-title":"IEEE Trans Image Process"},{"key":"14202_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou Q, Yu L, Chen H et al (2017) 3D deeply supervised network for automated segmentation of volumetric medical images. Med Image Anal 41:40\u201354","journal-title":"Med Image Anal"},{"key":"14202_CR15","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1109\/TMI.2019.2894322","volume":"38","author":"J Duan","year":"2019","unstructured":"Duan J, Bello G, Schlemper J et al (2019) Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Trans Med Imaging 38:2151\u20132164","journal-title":"IEEE Trans Med Imaging"},{"key":"14202_CR16","doi-asserted-by":"crossref","unstructured":"Fan DP, Wang W, Cheng MM, Shen J (2019) Shifting more attention to video salient object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8554\u20138564","DOI":"10.1109\/CVPR.2019.00875"},{"key":"14202_CR17","doi-asserted-by":"publisher","first-page":"101551","DOI":"10.1016\/j.media.2019.101551","volume":"58","author":"PA Ganaye","year":"2019","unstructured":"Ganaye PA, Sdika M, Triggs B et al (2019) Removing segmentation inconsistencies with semi-supervised non-adjacency constraint. Med Image Anal 58:101551. https:\/\/doi.org\/10.1016\/j.media.2019.101551","journal-title":"Med Image Anal"},{"key":"14202_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1080\/24699322.2019.1649071","volume":"24","author":"L Geng","year":"2019","unstructured":"Geng L, Zhang S, Tong J, Xiao Z (2019) Lung segmentation method with dilated convolution based on VGG-16 network. Computer Assisted Surgery 24:27\u201333","journal-title":"Computer Assisted Surgery"},{"key":"14202_CR19","doi-asserted-by":"publisher","first-page":"107764","DOI":"10.1016\/j.patcog.2020.107764","volume":"112","author":"D Guan","year":"2021","unstructured":"Guan D, Huang J, Lu S, Xiao A (2021) Scale variance minimization for unsupervised domain adaptation in image segmentation. Pattern Recogn 112:107764. https:\/\/doi.org\/10.1016\/j.patcog.2020.107764","journal-title":"Pattern Recogn"},{"issue":"1","key":"14202_CR20","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/JAS.2020.1003387","volume":"8","author":"C Ieracitano","year":"2021","unstructured":"Ieracitano C, Paviglianiti A, Campolo M, Hussain A, Pasero E, Morabito FC (2021) A novel automatic classification system based on hybrid unsupervised and supervised machine learning for electrospun nanofibers. IEEE\/CAA Journal of Automatica Sinica 8(1):64\u201376. https:\/\/doi.org\/10.1109\/JAS.2020.1003387","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"14202_CR21","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.media.2015.06.012","volume":"24","author":"JE Iglesias","year":"2015","unstructured":"Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24:205\u2013219","journal-title":"Med Image Anal"},{"key":"14202_CR22","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Full PM et al (2017) Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 120\u2013129","DOI":"10.1007\/978-3-319-75541-0_13"},{"key":"14202_CR23","doi-asserted-by":"crossref","unstructured":"Islam MA, Rochan M, Bruce NDB, Wang Y (2017) Gated feedback refinement network for dense image labeling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3751\u20133759","DOI":"10.1109\/CVPR.2017.518"},{"key":"14202_CR24","doi-asserted-by":"crossref","unstructured":"J\u00e9gou S, Drozdzal M, Vazquez D, Romero A, Bengio Y (2017) The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 11\u201319","DOI":"10.1109\/CVPRW.2017.156"},{"key":"14202_CR25","doi-asserted-by":"crossref","unstructured":"Khened M, Alex V, Krishnamurthi G (2017) Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 140\u2013151","DOI":"10.1007\/978-3-319-75541-0_15"},{"key":"14202_CR26","doi-asserted-by":"publisher","unstructured":"Kirisli HA, Schaap M, Klein S et al (2010) Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach. In: Medical Imaging 2010: Image Processing. International Society for Optics and Photonics. 7623:762305. https:\/\/doi.org\/10.1117\/12.838370","DOI":"10.1117\/12.838370"},{"key":"14202_CR27","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"14202_CR28","doi-asserted-by":"crossref","unstructured":"Kudo Y, Aoki Y (2017) Dilated convolutions for image classification and object localization. In: 2017 Fifteenth IAPR international conference on machine vision applications (MVA). IEEE, pp 452\u2013455","DOI":"10.23919\/MVA.2017.7986898"},{"key":"14202_CR29","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"14202_CR30","doi-asserted-by":"crossref","unstructured":"Lieman-Sifry J, Le M, Lau F, Sall S, Golden D (2017) FastVentricle: cardiac segmentation with ENet. In: International conference on functional imaging and modeling of the heart. Springer, pp 127\u2013138","DOI":"10.1007\/978-3-319-59448-4_13"},{"key":"14202_CR31","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"14202_CR32","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1109\/TMI.2020.3003240","volume":"39","author":"N Painchaud","year":"2020","unstructured":"Painchaud N, Skandarani Y, Judge T et al (2020) Cardiac segmentation with strong anatomical guarantees. IEEE Trans Med Imaging 39:3703\u20133713","journal-title":"IEEE Trans Med Imaging"},{"key":"14202_CR33","doi-asserted-by":"crossref","unstructured":"Poudel RPK, Lamata P, Montana G (2016) Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation. In: Reconstruction, segmentation, and analysis of medical images. Springer, pp 83\u201394","DOI":"10.1007\/978-3-319-52280-7_8"},{"key":"14202_CR34","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"},{"key":"14202_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-020-00295-9","volume":"1","author":"CFGD Santos","year":"2020","unstructured":"Santos CFGD, Moreira TP, Colombo D et al (2020) Does removing pooling layers from convolutional neural networks improve results? SN Computer Science 1:1\u201310","journal-title":"SN Computer Science"},{"key":"14202_CR36","unstructured":"Skandarani Y, Painchaud N, Jodoin PM, Lalande A (2020) On the effectiveness of GAN generated cardiac MRIs for segmentation. arXiv:2005.09026"},{"key":"14202_CR37","doi-asserted-by":"publisher","unstructured":"Tran PV (2016) A fully convolutional neural network for cardiac segmentation in short-axis MRI. https:\/\/doi.org\/10.48550\/arXiv.1604.00494. arXiv:1604.00494","DOI":"10.48550\/arXiv.1604.00494"},{"key":"14202_CR38","doi-asserted-by":"publisher","first-page":"7145","DOI":"10.1007\/s11042-020-10111-4","volume":"80","author":"I Ullah","year":"2020","unstructured":"Ullah I, Jian M, Hussain S et al (2020) DSFMA: Deeply supervised fully convolutional neural networks based on multi-level aggregation for saliency detection. Multimed Tools Appl 80:7145\u20137165","journal-title":"Multimed Tools Appl"},{"key":"14202_CR39","doi-asserted-by":"publisher","unstructured":"Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1451\u20131460, DOI https:\/\/doi.org\/10.1109\/WACV.2018.00163, (to appear in print)","DOI":"10.1109\/WACV.2018.00163"},{"key":"14202_CR40","doi-asserted-by":"publisher","first-page":"27115","DOI":"10.1007\/s11042-020-09334-2","volume":"79","author":"Z Wei","year":"2020","unstructured":"Wei Z, Shi F, Song H, Ji W, Han G (2020) Attentive boundary aware network for multi-scale skin lesion segmentation with adversarial training. Multimed Tools Appl 79:27115\u201327136","journal-title":"Multimed Tools Appl"},{"key":"14202_CR41","unstructured":"Wu H, Zhang J, Huang K, Liang K, Yu Y (2019) Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation. arXiv:1903.11816"},{"issue":"33","key":"14202_CR42","doi-asserted-by":"publisher","first-page":"24225","DOI":"10.1007\/s11042-020-09110-2","volume":"79","author":"P Xia","year":"2020","unstructured":"Xia P, He J, Yin J (2020) Boosting image caption generation with feature fusion module. Multimed Tools Appl 79(33):24225\u201324239","journal-title":"Multimed Tools Appl"},{"issue":"33","key":"14202_CR43","doi-asserted-by":"publisher","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","volume":"79","author":"Y Xiao","year":"2020","unstructured":"Xiao Y, Tian Z, Yu J et al (2020) A review of object detection based on deep learning. Multimed Tools Appl 79(33):23729\u201323791","journal-title":"Multimed Tools Appl"},{"key":"14202_CR44","doi-asserted-by":"crossref","unstructured":"Yamashita T, Furukawa H, Fujiyoshi H (2018) Multiple skip connections of dilated convolution network for semantic segmentation. In: 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, pp 1593\u20131597","DOI":"10.1109\/ICIP.2018.8451033"},{"key":"14202_CR45","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122"},{"key":"14202_CR46","doi-asserted-by":"crossref","unstructured":"Zhang Q, Cui Z, Niu X, Geng S, Qiao Y (2017) Image segmentation with pyramid dilated convolution based on ResNet and U-Net. In: International Conference on Neural Information Processing. Springer, pp 364\u2013372","DOI":"10.1007\/978-3-319-70096-0_38"},{"issue":"13","key":"14202_CR47","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.3390\/app9132686","volume":"9","author":"J Zhang","year":"2019","unstructured":"Zhang J, Lu C, Wang J et al (2019) Concrete cracks detection based on FCN with dilated convolution. Appl Sci 9(13):2686. https:\/\/doi.org\/10.3390\/app9132686","journal-title":"Appl Sci"},{"key":"14202_CR48","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.radonc.2016.11.016","volume":"122","author":"R Zhou","year":"2017","unstructured":"Zhou R, Liao Z, Pan T et al (2017) Cardiac atlas development and validation for automatic segmentation of cardiac substructures. Radiother Oncol 122:66\u201371","journal-title":"Radiother Oncol"},{"key":"14202_CR49","doi-asserted-by":"crossref","unstructured":"Zhou L, Zhang C, Wu M (2018) D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 182\u2013186","DOI":"10.1109\/CVPRW.2018.00034"},{"issue":"7","key":"14202_CR50","doi-asserted-by":"publisher","first-page":"3822","DOI":"10.1118\/1.4921366","volume":"42","author":"X Zhuang","year":"2015","unstructured":"Zhuang X, Bai W, Song J, Zhan S, Qian X, Shi W, Lian Y, Rueckert D (2015) Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Med Phys 42(7):3822\u20133833","journal-title":"Med Phys"},{"key":"14202_CR51","doi-asserted-by":"publisher","first-page":"101537","DOI":"10.1016\/j.media.2019.101537","volume":"58","author":"X Zhuang","year":"2019","unstructured":"Zhuang X, Li L, Payer C et al (2019) Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med Image Anal 58:101537. https:\/\/doi.org\/10.1016\/j.media.2019.101537","journal-title":"Med Image Anal"},{"key":"14202_CR52","doi-asserted-by":"publisher","first-page":"1119","DOI":"10.1109\/JBHI.2018.2865450","volume":"23","author":"C Zotti","year":"2018","unstructured":"Zotti C, Luo Z, Lalande A et al (2018) Convolutional neural network with shape prior applied to cardiac MRI segmentation. IEEE J Biomed Health Inform 23:1119\u20131128","journal-title":"IEEE J Biomed Health Inform"},{"key":"14202_CR53","doi-asserted-by":"crossref","unstructured":"Zuluaga MA, Cardoso MJ, Modat M, Ourselin S (2013) Multi-atlas propagation whole heart segmentation from MRI and CTA using a local normalised correlation coefficient criterion. In: International conference on functional imaging and modeling of the heart. Springer, pp 174\u2013181","DOI":"10.1007\/978-3-642-38899-6_21"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14202-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14202-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14202-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T09:21:13Z","timestamp":1685006473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14202-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":53,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["14202"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14202-2","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,12,6]]},"assertion":[{"value":"7 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}