{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T22:09:13Z","timestamp":1775167753730,"version":"3.50.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T00:00:00Z","timestamp":1644278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61976126"],"award-info":[{"award-number":["61976126"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019MF003"],"award-info":[{"award-number":["ZR2019MF003"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Automated segmentation of cardiac pathology in MRI plays a significant role for diagnosis and treatment of some cardiac disease. In clinical practice, multi-modality MRI is widely used to improve the cardiac pathology segmentation, because it can provide multiple or complementary information. Recently, deep learning methods have presented implausible performance in multi-modality medical image segmentation. However, how to fuse the underlying multi-modality information effectively to segment the pathology with irregular shapes and small region at random locations, is still a challenge task. In this paper, a triple-attention-based multi-modality MRI fusion U-Net was proposed to learn complex relationship between different modalities and pay more attention on shape information, thus to achieve improved pathology segmentation. First, three independent encoders and one fusion encoder were applied to extract specific and multiple modality features. Secondly, we concatenate the modality feature maps and use the channel attention to fuse specific modal information at every stage of the three dedicate independent encoders, then the three single modality feature maps and channel attention feature maps are together concatenated to the decoder path. Spatial attention was adopted in decoder path to capture the correlation of various positions. Once more, we employ shape attention to focus shape-dependent information. Lastly, the training approach is made efficient by introducing deep supervision mechanism with object contextual representations block to ensure precisely boundary prediction. Our proposed network was evaluated on the public MICCAI 2020 Myocardial pathology segmentation dataset which involves patients suffering from myocardial infarction. Experiments on the dataset with three modalities demonstrate the effectiveness of fusion mode of our model, and attention mechanism can integrate various modality information well. We demonstrated that such a deep learning approach could better fuse complementary information to improve the segmentation performance of cardiac pathology.<\/jats:p>","DOI":"10.1007\/s40747-022-00660-6","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T07:02:51Z","timestamp":1644303771000},"page":"2489-2505","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation"],"prefix":"10.1007","volume":"8","author":[{"given":"Dapeng","family":"Li","sequence":"first","affiliation":[]},{"given":"Yanjun","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Yanfei","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jindong","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,8]]},"reference":[{"key":"660_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.artmed.2018.11.007","volume":"97","author":"AM Anter","year":"2019","unstructured":"Anter AM, Hassenian AE (2019) Ct liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm. Artif Intell Med 97:105\u2013117","journal-title":"Artif Intell Med"},{"key":"660_CR2","doi-asserted-by":"crossref","unstructured":"Chen C, Ouyang C, Tarroni G, Schlemper J, Qiu H, Bai W, Rueckert D (2019) Unsupervised multi-modal style transfer for cardiac MR segmentation. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 209\u2013219","DOI":"10.1007\/978-3-030-39074-7_22"},{"key":"660_CR3","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2017.04.041","volume":"170","author":"H Chen","year":"2018","unstructured":"Chen H, Dou Q, Yu L, Qin J, Heng PA (2018) Voxresnet: deep voxelwise residual networks for brain segmentation from 3d MR images. NeuroImage 170:446\u2013455","journal-title":"NeuroImage"},{"key":"660_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.neucom.2020.06.078","volume":"412","author":"Y Ding","year":"2020","unstructured":"Ding Y, Gong L, Zhang M, Li C, Qin Z (2020) A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing 412:19\u201330","journal-title":"Neurocomputing"},{"issue":"5","key":"660_CR5","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","volume":"38","author":"J Dolz","year":"2018","unstructured":"Dolz J, Gopinath K, Yuan J, Lombaert H, Desrosiers C, Ayed IB (2018) Hyperdense-net: a hyper-densely connected CNN for multi-modal image segmentation. IEEE Trans Med Imaging 38(5):1116\u20131126","journal-title":"IEEE Trans Med Imaging"},{"key":"660_CR6","first-page":"128","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"A Elif","year":"2020","unstructured":"Elif A, Ilkay O (2020) Accurate myocardial pathology segmentation with residual u-net. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 128\u2013137"},{"issue":"11","key":"660_CR7","doi-asserted-by":"publisher","first-page":"3619","DOI":"10.1109\/TMI.2020.3001036","volume":"39","author":"X Fang","year":"2020","unstructured":"Fang X, Yan P (2020) Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Trans Med Imaging 39(11):3619\u20133629","journal-title":"IEEE Trans Med Imaging"},{"key":"660_CR8","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"10","key":"660_CR9","doi-asserted-by":"publisher","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","volume":"38","author":"Z Gu","year":"2019","unstructured":"Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, Zhang T, Gao S, Liu J (2019) Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans Med Imaging 38(10):2281\u20132292","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"660_CR10","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TRPMS.2018.2890359","volume":"3","author":"Z Guo","year":"2019","unstructured":"Guo Z, Li X, Huang H, Guo N, Li Q (2019) Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci 3(2):162\u2013169","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"660_CR11","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18\u201331","journal-title":"Med Image Anal"},{"key":"660_CR12","doi-asserted-by":"publisher","first-page":"8187","DOI":"10.1109\/TIP.2020.3011557","volume":"29","author":"Y Huang","year":"2020","unstructured":"Huang Y, Zheng F, Cong R, Huang W, Scott MR, Shao L (2020) Mcmt-gan: multi-task coherent modality transferable gan for 3d brain image synthesis. IEEE Trans Image Process 29:8187\u20138198","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"660_CR13","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/TMI.2018.2881110","volume":"38","author":"Y Huo","year":"2018","unstructured":"Huo Y, Xu Z, Bao S, Bermudez C, Moon H, Parvathaneni P, Moyo TK, Savona MR, Assad A, Abramson RG et al (2018) Splenomegaly segmentation on multi-modal MRI using deep convolutional networks. IEEE Trans Med Imaging 38(5):1185\u20131196","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"660_CR14","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1002\/jemt.22994","volume":"81","author":"S Iqbal","year":"2018","unstructured":"Iqbal S, Ghani MU, Saba T, Rehman A (2018) Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN). Microsc Res Tech 81(4):419\u2013427","journal-title":"Microsc Res Tech"},{"key":"660_CR15","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Full PM, Wolf I, Engelhardt S, Maier-Hein KH (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":"660_CR16","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":"660_CR17","first-page":"68","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"H Jiang","year":"2020","unstructured":"Jiang H, Wang C, Chartsias A, Tsaftaris SA (2020) Max-fusion u-net for multi-modal pathology segmentation with attention and dynamic resampling. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 68\u201381"},{"key":"660_CR18","doi-asserted-by":"crossref","unstructured":"Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D, et\u00a0al. (2017) Ensembles of multiple models and architectures for robust brain tumour segmentation. In: International MICCAI brainlesion workshop. Springer, pp 450\u2013462","DOI":"10.1007\/978-3-319-75238-9_38"},{"key":"660_CR19","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":"660_CR20","first-page":"146","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"F Li","year":"2020","unstructured":"Li F, Li W (2020) Dual-path feature aggregation network combined multi-layer fusion for myocardial pathology segmentation with multi-sequence cardiac mr. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 146\u2013158"},{"key":"660_CR21","doi-asserted-by":"publisher","first-page":"106776","DOI":"10.1016\/j.knosys.2021.106776","volume":"215","author":"F Li","year":"2021","unstructured":"Li F, Li W, Qin S, Wang L (2021) Mdfa-net: multiscale dual-path feature aggregation network for cardiac segmentation on multi-sequence cardiac MR. Knowl-Based Syst 215:106776","journal-title":"Knowl-Based Syst"},{"key":"660_CR22","doi-asserted-by":"crossref","unstructured":"Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 510\u2013519","DOI":"10.1109\/CVPR.2019.00060"},{"key":"660_CR23","doi-asserted-by":"publisher","first-page":"101785","DOI":"10.1016\/j.compmedimag.2020.101785","volume":"85","author":"X Liao","year":"2020","unstructured":"Liao X, Qian Y, Chen Y, Xiong X, Wang Q, Heng PA (2020) Mmtlnet: multi-modality transfer learning network with adversarial training for 3d whole heart segmentation. Comput Med Imaging Graph 85:101785","journal-title":"Comput Med Imaging Graph"},{"key":"660_CR24","first-page":"26","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"Y Liu","year":"2020","unstructured":"Liu Y, Zhang M, Zhan Q, Gu D, Liu G (2020) Two-stage method for segmentation of the myocardial scars and edema on multi-sequence cardiac magnetic resonance. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 26\u201336"},{"key":"660_CR25","doi-asserted-by":"crossref","unstructured":"Ly B, Cochet H, Sermesant M (2019) Style data augmentation for robust segmentation of multi-modality cardiac MRI. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 197\u2013208","DOI":"10.1007\/978-3-030-39074-7_21"},{"issue":"8","key":"660_CR26","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TMI.2019.2911588","volume":"38","author":"Y Man","year":"2019","unstructured":"Man Y, Huang Y, Feng J, Li X, Wu F (2019) Deep q learning driven CT pancreas segmentation with geometry-aware u-net. IEEE Trans Med Imaging 38(8):1971\u20131980","journal-title":"IEEE Trans Med Imaging"},{"key":"660_CR27","first-page":"1","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"C Mart\u00edn-Isla","year":"2020","unstructured":"Mart\u00edn-Isla C, Asadi-Aghbolaghi M, Gkontra P, Campello VM, Escalera S, Lekadir K (2020) Stacked BCDU-net with semantic CMR synthesis: application to myocardial pathology segmentation challenge. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 1\u201316"},{"key":"660_CR28","doi-asserted-by":"crossref","unstructured":"Nie D, Wang L, Gao Y, Shen D (2016) Fully convolutional networks for multi-modality isointense infant brain image segmentation. In: 2016 IEEE 13Th international symposium on biomedical imaging (ISBI), IEEE, pp 1342\u20131345","DOI":"10.1109\/ISBI.2016.7493515"},{"key":"660_CR29","doi-asserted-by":"publisher","first-page":"102078","DOI":"10.1016\/j.media.2021.102078","volume":"20","author":"C Pei","year":"2021","unstructured":"Pei C, Wu F, Huang L, Zhuang X (2021) Disentangle domain features for cross-modality cardiac image segmentation. Med Image Anal 20:102078","journal-title":"Med Image Anal"},{"key":"660_CR30","doi-asserted-by":"publisher","first-page":"103815","DOI":"10.1016\/j.compbiomed.2020.103815","volume":"123","author":"R Rahmat","year":"2020","unstructured":"Rahmat R, Saednia K, Khani MRHH, Rahmati M, Jena R, Price SJ (2020) Multi-scale segmentation in GBM treatment using diffusion tensor imaging. Comput Biol Med 123:103815","journal-title":"Comput Biol Med"},{"key":"660_CR31","first-page":"20","volume":"20","author":"A Sinha","year":"2020","unstructured":"Sinha A, Dolz J (2020) Multi-scale self-guided attention for medical image segmentation. IEEE J Biomed Health Inform 20:20","journal-title":"IEEE J Biomed Health Inform"},{"issue":"3","key":"660_CR32","doi-asserted-by":"publisher","first-page":"e149","DOI":"10.1016\/j.ejrad.2009.05.035","volume":"74","author":"M Spiewak","year":"2010","unstructured":"Spiewak M, Malek LA, Misko J, Chojnowska L, Milosz B, Klopotowski M, Petryka J, Dabrowski M, Kepka C, Ruzyllo W (2010) Comparison of different quantification methods of late gadolinium enhancement in patients with hypertrophic cardiomyopathy. Eur J Radiol 74(3):e149\u2013e153","journal-title":"Eur J Radiol"},{"key":"660_CR33","doi-asserted-by":"crossref","unstructured":"Sun J, Darbehani F, Zaidi M, Wang B (2020) Saunet: shape attentive u-net for interpretable medical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 797\u2013806","DOI":"10.1007\/978-3-030-59719-1_77"},{"key":"660_CR34","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2020.10.031","volume":"423","author":"J Sun","year":"2021","unstructured":"Sun J, Peng Y, Guo Y, Li D (2021) Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3d FCN. Neurocomputing 423:34\u201345","journal-title":"Neurocomputing"},{"key":"660_CR35","doi-asserted-by":"crossref","unstructured":"Takikawa T, Acuna D, Jampani V, Fidler S (2019) Gated-scnn: Gated shape CNNS for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 5229\u20135238","DOI":"10.1109\/ICCV.2019.00533"},{"key":"660_CR36","first-page":"20","volume":"20","author":"D Tomar","year":"2021","unstructured":"Tomar D, Lortkipanidze M, Vray G, Bozorgtabar B, Thiran JP (2021) Self-attentive spatial adaptive normalization for cross-modality domain adaptation. IEEE Trans Med Imaging 20:20","journal-title":"IEEE Trans Med Imaging"},{"key":"660_CR37","doi-asserted-by":"crossref","unstructured":"Wang G, Li W, Ourselin S, Vercauteren T (2017) Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. In: International MICCAI brainlesion workshop. Springer, pp 178\u2013190","DOI":"10.1007\/978-3-319-75238-9_16"},{"key":"660_CR38","doi-asserted-by":"crossref","unstructured":"Wang J, Huang H, Chen C, Ma W, Huang Y, Ding X (2019a) Multi-sequence cardiac MR segmentation with adversarial domain adaptation network. In: International workshop on statistical atlases and computational models of the heart. Springer, pp 254\u2013262","DOI":"10.1007\/978-3-030-39074-7_27"},{"key":"660_CR39","first-page":"20","volume":"20","author":"J Wang","year":"2020","unstructured":"Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Mu Y, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 20:20","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"660_CR40","first-page":"20","volume":"20","author":"X Wang","year":"2021","unstructured":"Wang X, Yang S, Fang Y, Wei Y, Wang M, Zhang J, Han X (2021) Sk-unet: an improved u-net model with selective kernel for the segmentation of LGE cardiac MR images. IEEE Sens J 20:20","journal-title":"IEEE Sens J"},{"key":"660_CR41","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.compmedimag.2019.04.001","volume":"75","author":"Y Wang","year":"2019","unstructured":"Wang Y, Li C, Zhu T, Zhang J (2019) Multimodal brain tumor image segmentation using wrn-ppnet. Comput Med Imaging Graph 75:56\u201365","journal-title":"Comput Med Imaging Graph"},{"issue":"12","key":"660_CR42","doi-asserted-by":"publisher","first-page":"4274","DOI":"10.1109\/TMI.2020.3016144","volume":"39","author":"F Wu","year":"2020","unstructured":"Wu F, Zhuang X (2020) Cf distance: a new domain discrepancy metric and application to explicit domain adaptation for cross-modality cardiac image segmentation. IEEE Trans Med Imaging 39(12):4274\u20134285","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"660_CR43","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1002\/mp.12832","volume":"45","author":"G Yang","year":"2018","unstructured":"Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Li L, Wage R, Ye X, Slabaugh G, Mohiaddin R et al (2018) Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced mri. Med Phys 45(4):1562\u20131576","journal-title":"Med Phys"},{"key":"660_CR44","first-page":"118","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"H Yu","year":"2020","unstructured":"Yu H, Zha S, Huangfu Y, Chen C, Ding M, Li J (2020) Dual attention u-net for multi-sequence cardiac MR images segmentation. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 118\u2013127"},{"key":"660_CR45","doi-asserted-by":"crossref","unstructured":"Yuan Y, Chen X, Chen X, Wang J (2021) Segmentation transformer: object-contextual representations for semantic segmentation. In: European conference on computer vision (ECCV), vol\u00a01","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"660_CR46","first-page":"49","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"S Zhai","year":"2020","unstructured":"Zhai S, Gu R, Lei W, Wang G (2020) Myocardial edema and scar segmentation using a coarse-to-fine framework with weighted ensemble. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 49\u201359"},{"key":"660_CR47","doi-asserted-by":"publisher","first-page":"9032","DOI":"10.1109\/TIP.2020.3023609","volume":"29","author":"D Zhang","year":"2020","unstructured":"Zhang D, Huang G, Zhang Q, Han J, Han J, Wang Y, Yu Y (2020) Exploring task structure for brain tumor segmentation from multi-modality MR images. IEEE Trans Image Process 29:9032\u20139043","journal-title":"IEEE Trans Image Process"},{"key":"660_CR48","doi-asserted-by":"publisher","first-page":"102005","DOI":"10.1016\/j.media.2021.102005","volume":"70","author":"D Zhang","year":"2021","unstructured":"Zhang D, Chen B, Chong J, Li S (2021) Weakly-supervised teacher-student network for liver tumor segmentation from non-enhanced images. Med Image Anal 70:102005","journal-title":"Med Image Anal"},{"key":"660_CR49","first-page":"17","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"J Zhang","year":"2020","unstructured":"Zhang J, Xie Y, Liao Z, Verjans J, Xia Y (2020) Efficientseg: a simple but efficient solution to myocardial pathology segmentation challenge. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 17\u201325"},{"issue":"9","key":"660_CR50","doi-asserted-by":"publisher","first-page":"2782","DOI":"10.1109\/TMI.2020.2975347","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang L, Zhang J, Shen P, Zhu G, Li P, Lu X, Zhang H, Shah SA, Bennamoun M (2020) Block level skip connections across cascaded v-net for multi-organ segmentation. IEEE Trans Med Imaging 39(9):2782\u20132793","journal-title":"IEEE Trans Med Imaging"},{"key":"660_CR51","first-page":"82","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"X Zhang","year":"2020","unstructured":"Zhang X, Noga M, Punithakumar K (2020) Fully automated deep learning based segmentation of normal, infarcted and edema regions from multiple cardiac MRI sequences. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 82\u201391"},{"key":"660_CR52","doi-asserted-by":"publisher","first-page":"101884","DOI":"10.1016\/j.media.2020.101884","volume":"68","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Wu J, Liu Y, Chen Y, Chen W, Wu EX, Li C, Tang X (2021) A deep learning framework for pancreas segmentation with multi-atlas registration and 3d level-set. Med Image Anal 68:101884","journal-title":"Med Image Anal"},{"key":"660_CR53","first-page":"37","volume-title":"Myocardial pathology segmentation combining multi-sequence CMR challenge","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Liu C, Ding W, Wang S, Pei C, Yang M, Huang L (2020) Multi-modality pathology segmentation framework: application to cardiac magnetic resonance images. Myocardial pathology segmentation combining multi-sequence CMR challenge. Springer, Berlin, pp 37\u201348"},{"key":"660_CR54","doi-asserted-by":"publisher","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","volume":"3","author":"T Zhou","year":"2019","unstructured":"Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 3:100004","journal-title":"Array"},{"issue":"12","key":"660_CR55","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"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00660-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-022-00660-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00660-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,23]],"date-time":"2022-10-23T14:24:42Z","timestamp":1666535082000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-022-00660-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,8]]},"references-count":55,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["660"],"URL":"https:\/\/doi.org\/10.1007\/s40747-022-00660-6","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,8]]},"assertion":[{"value":"5 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work was supported in part by the National Natural Science Foundation of China (Grant no. 61976126), Shandong Nature Science Foundation of China (nos. ZR2019MF003, ZR2017MF054, ZR2020MH132, ZR2020MF291).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Funding"}},{"value":"The authors declare there are no conflicts of interest regarding the publication of this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Data related to the current study are available from the corresponding author on reasonable request.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Availability of data and materials"}},{"value":"The codes used during the study are available from the corresponding author by request.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}