{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T14:56:35Z","timestamp":1750085795862,"version":"3.37.3"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T00:00:00Z","timestamp":1662249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LQ21H190004"],"award-info":[{"award-number":["LQ21H190004"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2020T130102ZX"],"award-info":[{"award-number":["2020T130102ZX"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100017940","name":"Zhejiang\u00a0Provincial\u00a0Postdoctoral\u00a0Science\u00a0Foundation","doi-asserted-by":"publisher","award":["ZJ2020031"],"award-info":[{"award-number":["ZJ2020031"]}],"id":[{"id":"10.13039\/100017940","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Educational Commission of Zhejiang Province of China","award":["Y202147553"],"award-info":[{"award-number":["Y202147553"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11554-022-01249-5","type":"journal-article","created":{"date-parts":[[2022,9,4]],"date-time":"2022-09-04T17:02:21Z","timestamp":1662310941000},"page":"1091-1104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["FAM: focal attention module for lesion segmentation of COVID-19 CT images"],"prefix":"10.1007","volume":"19","author":[{"given":"Xiaoxin","family":"Wu","sequence":"first","affiliation":[]},{"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lingling","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qiaojie","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Bei","family":"Jin","sequence":"additional","affiliation":[]},{"given":"Weiyan","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Fangfang","family":"Lu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1737-3420","authenticated-orcid":false,"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"issue":"2","key":"1249_CR1","doi-asserted-by":"publisher","first-page":"E32","DOI":"10.1148\/radiol.2020200642","volume":"296","author":"T Ai","year":"2020","unstructured":"Ai, T., Yang, Z., Hou, H., et al.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32\u2013E40 (2020). https:\/\/doi.org\/10.1148\/radiol.2020200642","journal-title":"Radiology"},{"issue":"5","key":"1249_CR2","doi-asserted-by":"publisher","first-page":"1885","DOI":"10.1016\/j.chest.2020.06.025","volume":"158","author":"HJ Adams","year":"2020","unstructured":"Adams, H.J., Kwee, T.C., Yakar, D., et al.: Chest CT imaging signature of coronavirus disease 2019 infection: in pursuit of the scientific evidence. Chest 158(5), 1885\u20131895 (2020). https:\/\/doi.org\/10.1016\/j.chest.2020.06.025","journal-title":"Chest"},{"key":"1249_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2022.3142265","author":"X Xu","year":"2022","unstructured":"Xu, X., Tian, H., Zhang, X., Qi, L., He, Q., Dou, W.: DisCOV: distributed COVID-19 detection on X-ray images with edge-cloud collaboration. IEEE Trans. Serv. Comput. (2022). https:\/\/doi.org\/10.1109\/TSC.2022.3142265","journal-title":"IEEE Trans. Serv. Comput."},{"issue":"4","key":"1249_CR4","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2016","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2016). https:\/\/doi.org\/10.1109\/TPAMI.2016.2572683","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"1249_CR5","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.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1249_CR6","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: \u201cU-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234\u2013241 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1249_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume-title":"Deep learning in medical image analysis and multimodal learning for clinical decision support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., et al.: Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3\u201311. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_1"},{"key":"1249_CR8","doi-asserted-by":"publisher","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., Wu, J.: Unet 3+: A full-scale connected UNET for medical image segmentation. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 1055\u20131059. (2020) https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053405","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"1249_CR9","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I. et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFS. (2014) [Online]. https:\/\/arxiv.org\/abs\/1412.7062"},{"issue":"4","key":"1249_CR10","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., et al.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFS. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2017). https:\/\/doi.org\/10.1109\/TPAMI.2017.2699184","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1249_CR11","unstructured":"Florian, L.-C., Adam, S. H.: Rethinking atrous convolution for semantic image segmentation. In: Conference on Computer Vision and Pattern Recognition (CVPR). IEEE\/CVF, (2017) [Online]. https:\/\/arxiv.org\/abs\/1706.05587"},{"issue":"8","key":"1249_CR12","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011\u20132023 (2020). https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1249_CR13","doi-asserted-by":"publisher","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I. S.: Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319 (2018) https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"04","key":"1249_CR14","doi-asserted-by":"publisher","first-page":"6422","DOI":"10.1609\/aaai.v34i04.6113","volume":"34","author":"W Wu","year":"2020","unstructured":"Wu, W., Zhang, Y., Wang, D., Lei, Y.: Sk-net: deep learning on point cloud via end-to-end discovery of spatial keypoints. Proc. AAAI Conf. Artif. Intell. 34(04), 6422\u20136429 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.6113","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"99","key":"1249_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMI.2020.2996645","volume":"PP","author":"DP Fan","year":"2020","unstructured":"Fan, D.P., Zhou, T., Ji, G.P., et al.: Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Trans. Med. Imaging PP(99), 1\u20131 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.2996645","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1249_CR16","unstructured":"Chen, X., Yao, L., Zhang, Y.: Residual attention u-net for automated multi-class segmentation of covid-19 chest CT images. (2020) [Online]. Available: https:\/\/arxiv.org\/abs\/2004.05645"},{"key":"1249_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108109","volume":"119","author":"S Zhao","year":"2021","unstructured":"Zhao, S., Li, Z., Chen, Y., et al.: SCOAT-Net: aa novel network for segmenting COVID-19 lung opacification from CT images. Pattern Recogn. 119, 108109 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.108109","journal-title":"Pattern Recogn."},{"issue":"8","key":"1249_CR18","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.1109\/TMI.2020.3000314","volume":"39","author":"G Wang","year":"2020","unstructured":"Wang, G., Liu, X., Li, C., et al.: A noise-robust framework for automatic segmentation of COVID-19 pneumonia lesions from CT images. IEEE Trans. Med. Imaging 39(8), 2653\u20132663 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3000314","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1249_CR19","unstructured":"Yan, Q., Wang, B., Gong, D. et al.; COVID-19 chest CT image segmentation\u2014a deep convolutional neural network solution. (2020) [Online]. Available: https:\/\/arxiv.org\/abs\/2004.10987"},{"key":"1249_CR20","doi-asserted-by":"crossref","unstructured":"Elharrouss, O., Subramanian, N., Al-Maadeed, S.: An encoder-decoder-based method for COVID-19 lung infection segmentation. (2020) [Online]. Available: https:\/\/arxiv.org\/abs\/2007.00861","DOI":"10.29117\/quarfe.2020.0294"},{"key":"1249_CR21","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Liu, Y., Li, S., Xu, J.: Miniseg: an extremely minimum network for efficient COVID-19 segmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 6, (2021) 4846\u20134854. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16617","DOI":"10.1609\/aaai.v35i6.16617"},{"key":"1249_CR22","doi-asserted-by":"publisher","first-page":"47144","DOI":"10.1109\/ACCESS.2021.3067047","volume":"9","author":"H-Y Pei","year":"2021","unstructured":"Pei, H.-Y., Yang, D., Liu, G.-R., et al.: MPS-net: multi-point supervised network for CT image segmentation of covid-19. IEEE Access 9, 47144\u201347153 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3067047","journal-title":"IEEE Access"},{"issue":"11","key":"1249_CR23","doi-asserted-by":"publisher","first-page":"901","DOI":"10.3390\/diagnostics10110901","volume":"10","author":"P Zhang","year":"2020","unstructured":"Zhang, P., Zhong, Y., Deng, Y., et al.: CoSinGAN: learning COVID-19 infection segmentation from a single radiological image. Diagnostics 10(11), 901 (2020). https:\/\/doi.org\/10.3390\/diagnostics10110901","journal-title":"Diagnostics"},{"issue":"11","key":"1249_CR24","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/34.730558","volume":"20","author":"L Itti","year":"1998","unstructured":"Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254\u20131259 (1998). https:\/\/doi.org\/10.1109\/34.730558","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1249_CR25","unstructured":"Wang, F., Tax, D. M.: Survey on the attention based RNN model and its applications in computer vision. (2016) [Online]. Available: https:\/\/arxiv.org\/abs\/1601.06823"},{"key":"1249_CR26","doi-asserted-by":"publisher","unstructured":"Jaderberg, M., Simonyan, K., Zisserman, A.: Spatial transformer networks. Adv. Neural Inform. Process. Syst. 28 (2015). https:\/\/dl.acm.org\/doi\/abs\/https:\/\/doi.org\/10.5555\/2969442.2969465","DOI":"10.5555\/2969442.2969465"},{"key":"1249_CR27","doi-asserted-by":"publisher","unstructured":"Buades, A., Coll, B., Morel, J.-M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), vol. 2. IEEE, pp. 60\u201365 (2005). https:\/\/doi.org\/10.1109\/CVPR.2005.38","DOI":"10.1109\/CVPR.2005.38"},{"key":"1249_CR28","doi-asserted-by":"publisher","unstructured":"Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7794\u20137803 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00813","DOI":"10.1109\/CVPR.2018.00813"},{"key":"1249_CR29","doi-asserted-by":"publisher","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3146\u20133154 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00326","DOI":"10.1109\/CVPR.2019.00326"},{"key":"1249_CR30","doi-asserted-by":"publisher","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., Tang, X.: Residual attention network for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156\u20133164 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.683","DOI":"10.1109\/CVPR.2017.683"},{"key":"1249_CR31","doi-asserted-by":"publisher","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., Liu, W.: Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 603\u2013612 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2020.3007032","DOI":"10.1109\/TPAMI.2020.3007032"},{"key":"1249_CR32","doi-asserted-by":"publisher","unstructured":"Gao, P., Zheng, M., Wang, X., Dai, J., Li, H.: Fast convergence of detr with spatially modulated co-attention (2021) [Online]. https:\/\/doi.org\/10.48550\/arXiv.2108.02404","DOI":"10.48550\/arXiv.2108.02404"},{"key":"1249_CR33","doi-asserted-by":"publisher","first-page":"122798","DOI":"10.1109\/ACCESS.2020.3007719","volume":"8","author":"G Huang","year":"2020","unstructured":"Huang, G., Zhu, J., Li, J., Wang, Z., Cheng, L., Liu, L., Li, H., Zhou, J.: Channel-attention U-Net: channel attention mechanism for semantic segmentation of esophagus and esophageal cancer. IEEE Access 8, 122798\u2013122810 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3007719","journal-title":"IEEE Access"},{"issue":"6","key":"1249_CR34","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TMM.2017.2648498","volume":"19","author":"B Zhao","year":"2017","unstructured":"Zhao, B., Wu, X., Feng, J., et al.: Diversified visual attention networks for fine-grained object classification. IEEE Trans. Multimed. 19(6), 1245\u20131256 (2017). https:\/\/doi.org\/10.1109\/TMM.2017.2648498","journal-title":"IEEE Trans. Multimed."},{"key":"1249_CR35","doi-asserted-by":"publisher","unstructured":"Mnih, V., Heess, N., Graves, A.: Recurrent models of visual attention. Adv. Neural Inform. processing Syst. 27 (2014). https:\/\/dl.acm.org\/doi\/abs\/https:\/\/doi.org\/10.5555\/2969033.2969073","DOI":"10.5555\/2969033.2969073"},{"key":"1249_CR36","unstructured":"Liu, X., Xia, T., Wang, J. et al.: Fully convolutional attention networks for fine-grained recognition. (2016) [Online]. https:\/\/arxiv.org\/abs\/1603.06765"},{"key":"1249_CR37","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, P., Song, F. et al.: D2a u-net: automatic segmentation of COVID-19 lesions from CT slices with dilated convolution and dual attention mechanism. (2021) [Online]. https:\/\/arxiv.org\/abs\/2102.05210","DOI":"10.1016\/j.compbiomed.2021.104526"},{"issue":"1","key":"1249_CR38","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1002\/ima.22527","volume":"31","author":"T Zhou","year":"2021","unstructured":"Zhou, T., Canu, S., Ruan, S.: Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int. J. Imaging Syst. Technol. 31(1), 16\u201327 (2021). https:\/\/doi.org\/10.1002\/ima.22527","journal-title":"Int. J. Imaging Syst. Technol."},{"issue":"16","key":"1249_CR39","doi-asserted-by":"publisher","first-page":"12588","DOI":"10.1109\/JIOT.2021.3077449","volume":"8","author":"X Zhou","year":"2021","unstructured":"Zhou, X., Xu, X., Liang, W., Zeng, Z., Yan, Z.: Deep-learning- enhanced multitarget detection for end-edge-cloud surveillance in smart IoT. IEEE Internet Things J. 8(16), 12588\u201312596 (2021). https:\/\/doi.org\/10.1109\/JIOT.2021.3077449","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"1249_CR40","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s11263-006-7533-5","volume":"69","author":"D Cremers","year":"2006","unstructured":"Cremers, D., Osher, S.J., Soatto, S.: Kernel density estimation and intrinsic alignment for shape priors in level set segmentation. Int. J. Comput. Vis. 69(3), 335\u2013351 (2006). https:\/\/doi.org\/10.1007\/s11263-006-7533-5","journal-title":"Int. J. Comput. Vis."},{"issue":"7","key":"1249_CR41","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1109\/TMM.2015.2433795","volume":"17","author":"K Li","year":"2015","unstructured":"Li, K., Tao, W.: Adaptive optimal shape prior for easy interactive object segmentation. IEEE Trans. Multimed. 17(7), 994\u20131005 (2015). https:\/\/doi.org\/10.1109\/TMM.2015.2433795","journal-title":"IEEE Trans. Multimed."},{"key":"1249_CR42","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, H.: Adaptive shape prior in graph cut segmentation. In: 2010 IEEE International Conference on Image Pro- cessing. IEEE, pp 3029\u20133032 (2010). https:\/\/doi.org\/10.1109\/ICIP.2010.5653335","DOI":"10.1109\/ICIP.2010.5653335"},{"key":"1249_CR43","doi-asserted-by":"publisher","unstructured":"Veksler, O.: Star shape prior for graph-cut image segmentation. In: European Conference on Computer Vision. Springer, pp 454\u2013467 (2008). https:\/\/doi.org\/10.1007\/978-3-540-88690-7_34","DOI":"10.1007\/978-3-540-88690-7_34"},{"key":"1249_CR44","unstructured":"Nosrati, M. S., Hamarneh, G.: Incorporating prior knowledge in medical image segmentation: a survey (2021) [Online]. Available: https:\/\/arxiv.org\/abs\/1607.01092"},{"issue":"11","key":"1249_CR45","doi-asserted-by":"publisher","first-page":"2596","DOI":"10.1109\/TMI.2019.2905990","volume":"38","author":"MCH Lee","year":"2019","unstructured":"Lee, M.C.H., Petersen, K., Pawlowski, N., Glocker, B., Schaap, M.: TeTrIS: template transformer networks for image segmentation with shape priors. IEEE Trans. Med. Imaging 38(11), 2596\u20132606 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2905990","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1249_CR46","doi-asserted-by":"publisher","unstructured":"Ravishankar, H., Venkataramani, R., Thiruvenkadam, S., Sudhakar, P., Vaidya, V.: Learning and incorporating shape models for semantic segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 203\u2013211 (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_24","DOI":"10.1007\/978-3-319-66182-7_24"},{"key":"1249_CR47","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.media.2016.01.005","volume":"30","author":"MR Avendi","year":"2016","unstructured":"Avendi, M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108\u2013119 (2016). https:\/\/doi.org\/10.1016\/j.media.2016.01.005","journal-title":"Med. Image Anal."},{"key":"1249_CR48","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.media.2016.05.009","volume":"35","author":"TA Ngo","year":"2017","unstructured":"Ngo, T.A., Lu, Z., Carneiro, G.: Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med. Image Anal. 35, 159\u2013171 (2017). https:\/\/doi.org\/10.1016\/j.media.2016.05.009","journal-title":"Med. Image Anal."},{"key":"1249_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108071","volume":"119","author":"C Zhao","year":"2021","unstructured":"Zhao, C., Xu, Y., He, Z., Tang, J., Zhang, Y., Han, J., Shi, Y., Zhou, W.: Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recogn. 119, 108071 (2021). https:\/\/doi.org\/10.1016\/j.patcog.2021.108071","journal-title":"Pattern Recogn."},{"issue":"4","key":"1249_CR50","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1145\/321356.321357","volume":"13","author":"A Rosenfeld","year":"1966","unstructured":"Rosenfeld, A., Pfaltz, J.L.: Sequential operations in digital picture processing. J. ACM (JACM) 13(4), 471\u2013494 (1966). https:\/\/doi.org\/10.1145\/321356.321357","journal-title":"J. ACM (JACM)"},{"issue":"2","key":"1249_CR51","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.cviu.2003.09.004","volume":"93","author":"FY Shih","year":"2004","unstructured":"Shih, F.Y., Wu, Y.-T.: Fast Euclidean distance transformation in two scans using a 3x3 neighborhood. Comput. Vis. Image Underst. 93(2), 195\u2013205 (2004). https:\/\/doi.org\/10.1016\/j.cviu.2003.09.004","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01249-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-022-01249-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-022-01249-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T13:44:51Z","timestamp":1666791891000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-022-01249-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,4]]},"references-count":51,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["1249"],"URL":"https:\/\/doi.org\/10.1007\/s11554-022-01249-5","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2022,9,4]]},"assertion":[{"value":"15 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 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":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}