{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T01:36:51Z","timestamp":1766281011751,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030390730"},{"type":"electronic","value":"9783030390747"}],"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_23","type":"book-chapter","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T16:03:02Z","timestamp":1579708982000},"page":"220-227","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Automatic Cardiac Segmentation Framework Based on Multi-sequence MR Image"],"prefix":"10.1007","author":[{"given":"Yashu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuanquan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengqin","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gongning","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"issue":"19","key":"23_CR1","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1161\/01.CIR.100.19.1992","volume":"100","author":"R Kim","year":"1999","unstructured":"Kim, R., et al.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100(19), 1992\u20132002 (1999)","journal-title":"Circulation"},{"key":"23_CR2","first-page":"267","volume":"21","author":"A Dastidar","year":"2016","unstructured":"Dastidar, A., et al.: Coronary artery disease imaging: what is the role of magnetic resonance imaging. Dialogues Cardiovasc. Med. 21, 267\u2013276 (2016)","journal-title":"Dialogues Cardiovasc. Med."},{"key":"23_CR3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.compmedimag.2017.05.001","volume":"59","author":"T Kurzendorfer","year":"2017","unstructured":"Kurzendorfer, T., et al.: Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI. Comput. Med. Imaging Graph. 59, 13\u201327 (2017)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"2","key":"23_CR4","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384\u2013395 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/978-3-030-00937-3_68","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"J Duan","year":"2018","unstructured":"Duan, J., et al.: Deep nested level sets: fully automated segmentation of cardiac MR images in patients with pulmonary hypertension. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 595\u2013603. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_68"},{"key":"23_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1007\/978-3-319-75541-0_15","volume-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges","author":"M Khened","year":"2018","unstructured":"Khened, M., Alex, V., Krishnamurthi, G.: Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest. In: Pop, M., et al. (eds.) STACOM 2017. LNCS, vol. 10663, pp. 140\u2013151. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-75541-0_15"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Duan, J., et al.: Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained multi-task deep learning approach. arXiv preprint. arXiv:1808.08578 (2018)","DOI":"10.1109\/TMI.2019.2894322"},{"key":"23_CR8","series-title":"Lecture Notes in Computer Science","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":"X Zhuang","year":"2016","unstructured":"Zhuang, X.: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 581\u2013588. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_67"},{"issue":"12","key":"23_CR9","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"Xiahai Zhuang","year":"2019","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 41, 2933\u20132946 (2018). https:\/\/doi.org\/10.1109\/tpami.2018.2869576","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"8","key":"23_CR10","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TMI.2018.2805821","volume":"37","author":"C Ma","year":"2018","unstructured":"Ma, C., et al.: Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans. Med. Imaging 37(8), 1943\u20131954 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"23_CR11","unstructured":"Kayalibay, B., et al.: CNN-based segmentation of medical imaging data. arXiv:1701.03056 (2017)"},{"key":"23_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-00937-3_71","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"S Dong","year":"2018","unstructured":"Dong, S., et al.: VoxelAtlasGAN: 3D left ventricle segmentation on echocardiography with atlas guided generation and voxel-to-voxel discrimination. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 622\u2013629. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_71"},{"issue":"9","key":"23_CR13","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1109\/TBME.2017.2762762","volume":"65","author":"G Luo","year":"2017","unstructured":"Luo, G., et al.: Multi-views fusion CNN for left ventricular volumes estimation on cardiac MR images. IEEE Trans. Biomed. Eng. 65(9), 1924\u20131934 (2017)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1002\/mrm.1910390222","volume":"39","author":"L Wang","year":"1998","unstructured":"Wang, L., et al.: Correction for variations in MRI scanner sensitivity in brain studies with histogram matching. Magn. Reson. Med. 39, 322\u2013327 (1998)","journal-title":"Magn. Reson. Med."},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"23_CR16","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"}],"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_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T23:04:55Z","timestamp":1737500695000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-39074-7_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030390730","9783030390747"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-39074-7_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}}]}}