{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T22:35:02Z","timestamp":1769553302030,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031317774","type":"print"},{"value":"9783031317781","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-31778-1_14","type":"book-chapter","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T06:02:18Z","timestamp":1683180138000},"page":"145-152","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automated Segmentation of\u00a0the\u00a0Left Atrium and\u00a0Scar Using Deep Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Kumaradevan","family":"Punithakumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelle","family":"Noga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"issue":"2","key":"14_CR1","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"14_CR2","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-1-4614-7657-3_19","volume-title":"Intraoperative Imaging and Image-Guided Therapy","author":"R Kikinis","year":"2014","unstructured":"Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Jolesz, F.A. (ed.) Intraoperative Imaging and Image-Guided Therapy, pp. 277\u2013289. Springer, New York (2014). https:\/\/doi.org\/10.1007\/978-1-4614-7657-3_19"},{"issue":"1","key":"14_CR3","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1161\/CIRCRESAHA.120.316340","volume":"127","author":"J Kornej","year":"2020","unstructured":"Kornej, J., B\u00f6rschel, C.S., Benjamin, E.J., Schnabel, R.B.: Epidemiology of atrial fibrillation in the 21st century: novel methods and new insights. Circ. Res. 127(1), 4\u201320 (2020)","journal-title":"Circ. Res."},{"key":"14_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/978-3-030-87231-1_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"L Li","year":"2021","unstructured":"Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: AtrialGeneral: domain generalization for left atrial segmentation of multi-center LGE MRIs. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12906, pp. 557\u2013566. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87231-1_54"},{"key":"14_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102303","volume":"76","author":"L Li","year":"2022","unstructured":"Li, L., Zimmer, V.A., Schnabel, J.A., Zhuang, X.: AtrialJSQnet: a new framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information. Med. Image Anal. 76, 102303 (2022)","journal-title":"Med. Image Anal."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (Oct 2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"14_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/978-3-319-66185-8_43","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2017","author":"A Mortazi","year":"2017","unstructured":"Mortazi, A., Karim, R., Rhode, K., Burt, J., Bagci, U.: CardiacNET: segmentation of left atrium and proximal pulmonary veins from MRI using multi-view CNN. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 377\u2013385. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66185-8_43"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Pop, M., et al.: Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges: 9th International Workshop, STACOM 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 16 September 2018, Revised Selected Papers, vol. 11395. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0","DOI":"10.1007\/978-3-030-12029-0"},{"key":"14_CR9","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":"14_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-030-12029-0_23","volume-title":"Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges","author":"Q Xia","year":"2019","unstructured":"Xia, Q., Yao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 211\u2013220. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_23"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, X., Noga, M., Martin, D., Punithakumar, K.: Fully automated left atrium segmentation from anatomical cine long-axis MRI sequences using deep convolutional neural network with unscented Kalman filter. Med. Image Anal. 68 (2021)","DOI":"10.1016\/j.media.2020.101916"},{"key":"14_CR12","doi-asserted-by":"crossref","unstructured":"Zhu, L., Gao, Y., Yezzi, A., MacLeod, R., Cates, J., Tannenbaum, A.: Automatic segmentation of the left atrium from MRI images using salient feature and contour evolution. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3211\u20133214. IEEE (2012)","DOI":"10.1109\/EMBC.2012.6346648"}],"container-title":["Lecture Notes in Computer Science","Left Atrial and Scar Quantification and Segmentation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-31778-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T19:16:18Z","timestamp":1729365378000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-31778-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031317774","9783031317781"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-31778-1_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LAScarQS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Challenge on Left Atrial and Scar Quantification and Segmentation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lascarqs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/zmiclab.github.io\/projects\/lascarqs22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}