{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T11:14:15Z","timestamp":1781522055622,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030390730","type":"print"},{"value":"9783030390747","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-39074-7_30","type":"book-chapter","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T16:03:02Z","timestamp":1579708982000},"page":"280-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Knowledge-Based Multi-sequence MR Segmentation via Deep Learning with\u00a0a\u00a0Hybrid U-Net++ Model"],"prefix":"10.1007","author":[{"given":"Jinchang","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","first-page":"1416","DOI":"10.1016\/j.jbiomech.2009.04.010","volume":"42","author":"H Gao","year":"2009","unstructured":"Gao, H., et al.: Carotid arterial plaque stress analysis using fluid-structure interactive simulation based onin-vivomagnetic resonance images of four patients. J. Biomech. 42, 1416\u20131423 (2009)","journal-title":"J. Biomech."},{"issue":"4","key":"30_CR2","doi-asserted-by":"publisher","first-page":"e004077","DOI":"10.1161\/CIRCIMAGING.115.004077","volume":"9","author":"A Schuster","year":"2016","unstructured":"Schuster, A., et al.: Cardiovascular magnetic resonance myocardial feature tracking concepts and clinical applications. Circ. Cardiovasc. Imaging 9(4), e004077 (2016)","journal-title":"Circ. Cardiovasc. Imaging"},{"key":"30_CR3","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1007\/978-3-319-46723-8_67","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"Xiahai Zhuang","year":"2016","unstructured":"Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens Greece, pp. 581\u2013588 (2016)"},{"key":"30_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2869576","author":"X Zhuang","year":"2018","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https:\/\/doi.org\/10.1109\/TPAMI.2018.2869576","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"2","key":"30_CR5","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/0031-3203(81)90009-1","volume":"13","author":"DH Ballard","year":"1981","unstructured":"Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recogn. 13(2), 111\u2013122 (1981)","journal-title":"Pattern Recogn."},{"issue":"1","key":"30_CR6","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","volume":"38","author":"F Meyer","year":"1994","unstructured":"Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113\u2013125 (1994)","journal-title":"Signal Process."},{"issue":"2","key":"30_CR7","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","volume":"10","author":"TF Chan","year":"2001","unstructured":"Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266\u2013277 (2001)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"30_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1006\/jcph.1994.1155","volume":"114","author":"M Sussman","year":"1994","unstructured":"Sussman, M., Smereka, P., Osher, S.: A level set approach for computing solutions to incompressible two-phase flow. J. Comput. Phys. 114(1), 146\u2013159 (1994)","journal-title":"J. Comput. Phys."},{"key":"30_CR9","unstructured":"Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035. Society for Industrial and Applied Mathematics (2007)"},{"key":"30_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ijitona, T.B., et al.: SAR sea ice image segmentation using watershed with intensity-based region merging. In: 2014 IEEE International Conference on Computer and Information Technology, Xi\u2019an, pp. 168\u2013172 (2014)","DOI":"10.1109\/CIT.2014.19"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Ren, J., et al.: Effective SAR sea ice image segmentation and touch floe separation using a combined multi-stage approach. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 1040\u20131043 (2015)","DOI":"10.1109\/IGARSS.2015.7325947"},{"key":"30_CR13","doi-asserted-by":"publisher","first-page":"10999","DOI":"10.1109\/ACCESS.2019.2891941","volume":"7","author":"X Xie","year":"2019","unstructured":"Xie, X., et al.: Automatic image segmentation with superpixels and image-level labels. IEEE Access 7, 10999\u201311009 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"30_CR14","doi-asserted-by":"publisher","first-page":"022007","DOI":"10.1117\/1.JRS.13.022007","volume":"13","author":"H Huang","year":"2019","unstructured":"Huang, H., et al.: Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images. J. Appl. Remote Sens. 13(2), 022007 (2019)","journal-title":"J. Appl. Remote Sens."},{"issue":"9","key":"30_CR15","doi-asserted-by":"publisher","first-page":"899","DOI":"10.3390\/rs9090899","volume":"9","author":"G Sun","year":"2017","unstructured":"Sun, G., et al.: Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. Remote Sens. 9(9), 899 (2017)","journal-title":"Remote Sens."},{"key":"30_CR16","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1525\/elementa.154","volume":"5","author":"B Hwang","year":"2017","unstructured":"Hwang, B., et al.: A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery. Elem. Sci. Anth. 5, 38 (2017)","journal-title":"Elem. Sci. Anth."},{"issue":"8","key":"30_CR17","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/TCSVT.2014.2381471","volume":"25","author":"J Han","year":"2015","unstructured":"Han, J., et al.: Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits Syst. Video Technol. 25(8), 1309\u20131321 (2015)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"6","key":"30_CR18","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","volume":"53","author":"J Han","year":"2015","unstructured":"Han, J., et al.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 53(6), 3325\u20133337 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"8","key":"30_CR19","doi-asserted-by":"publisher","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","volume":"53","author":"G Cheng","year":"2015","unstructured":"Cheng, G., et al.: Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(8), 4238\u20134249 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"12","key":"30_CR20","doi-asserted-by":"publisher","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","volume":"53","author":"L Fang","year":"2015","unstructured":"Fang, L., et al.: Classification of hyperspectral images by exploiting spectral-spatial information of superpixel via multiple kernels. IEEE Trans. Geosci. Remote Sens. 53(12), 6663\u20136673 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-39074-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T23:04:48Z","timestamp":1737500688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-39074-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030390730","9783030390747"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-39074-7_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"23 January 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"STACOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Statistical Atlases and Computational Models of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2019a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/stacom2019.cardiacatlas.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}