{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:42:20Z","timestamp":1774719740516,"version":"3.50.1"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030324551","type":"print"},{"value":"9783030324568","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:00:00Z","timestamp":1573084800000},"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\/978-3-030-32456-8_63","type":"book-chapter","created":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T14:03:42Z","timestamp":1573049022000},"page":"577-584","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Segmentation of Visible Epicardium Using Deep Learning in CT Image"],"prefix":"10.1007","author":[{"given":"Ziyu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yutaro","family":"Iwmoto","sequence":"additional","affiliation":[]},{"given":"Yuji","family":"Tezuka","sequence":"additional","affiliation":[]},{"given":"Hiroki","family":"Okada","sequence":"additional","affiliation":[]},{"given":"Kiyosumi","family":"Maeda","sequence":"additional","affiliation":[]},{"given":"Atsuyuki","family":"Wada","sequence":"additional","affiliation":[]},{"given":"Atsunori","family":"Kashiwagi","sequence":"additional","affiliation":[]},{"given":"Yen-Wei","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,7]]},"reference":[{"key":"63_CR1","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1161\/ATVBAHA.112.300829","volume":"33","author":"M Shimabukuro","year":"2013","unstructured":"Shimabukuro, M., et al.: Epicardial adipose tissue volume and adipocytokine imbalance are strongly linked to human coronary atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 33, 1077\u20131084 (2013)","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"63_CR2","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.atherosclerosis.2016.05.033","volume":"251","author":"MT Lu","year":"2016","unstructured":"Lu, M.T., et al.: Epicardial and paracardial adipose tissue volume and attenuation. Atherosclerosis 251, 47\u201354 (2016)","journal-title":"Atherosclerosis"},{"issue":"7","key":"63_CR3","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1093\/eurheartj\/ehn573","volume":"30","author":"AA Mahabadi","year":"2009","unstructured":"Mahabadi, A.A., et al.: Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: The Framingham heart study. Eur. Heart J. 30(7), 850\u2013856 (2009)","journal-title":"Eur. Heart J."},{"key":"63_CR4","doi-asserted-by":"publisher","first-page":"1835","DOI":"10.1109\/TMI.2018.2804799","volume":"37","author":"F Commandeur","year":"2018","unstructured":"Commandeur, F., et al.: Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT. IEEE Trans. Med. Imaging 37, 1835\u20131846 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"63_CR5","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1080\/10255842.2010.499871","volume":"14","author":"JG Barbosa","year":"2011","unstructured":"Barbosa, J.G., Figueiredo, B., Bettencourt, N., et al.: Towards automatic quantification of the epicardial fat in non-contrasted CT images. Comput. Methods Biomech. Biomed. Eng. 14(10), 905\u2013914 (2011)","journal-title":"Comput. Methods Biomech. Biomed. Eng."},{"key":"63_CR6","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","volume":"37","author":"X Li","year":"2018","unstructured":"Li, X., et al.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37, 2663\u20132674 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"63_CR7","series-title":"LNCS","first-page":"234","volume-title":"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)"},{"key":"63_CR8","doi-asserted-by":"crossref","unstructured":"Huang, G., et al.: Densely connected convolutional networks (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"63_CR9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation, pp. 1\u201311. arXiv (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"63_CR10","series-title":"LNCS","first-page":"240","volume-title":"DLMIA\/ML-CDS-2017","author":"CH Sudre","year":"2017","unstructured":"Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS-2017. LNCS, vol. 10553, pp. 240\u2013248. Springer, Cham (2017)"},{"key":"63_CR11","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2014)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"63_CR12","unstructured":"Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition (2005)"}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32456-8_63","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T17:21:59Z","timestamp":1573060919000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-32456-8_63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,7]]},"ISBN":["9783030324551","9783030324568"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32456-8_63","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"value":"2194-5357","type":"print"},{"value":"2194-5365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,7]]},"assertion":[{"value":"7 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICNC-FSKD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kunming","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":"20 July 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icncfskd2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}