{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:46:36Z","timestamp":1775263596520,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T00:00:00Z","timestamp":1593129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NFSC","award":["61701506"],"award-info":[{"award-number":["61701506"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,26]]},"DOI":"10.1145\/3430199.3430203","type":"proceedings-article","created":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T02:08:59Z","timestamp":1608602939000},"page":"84-87","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multi-view Learning for 3D LGE-MRI Left Atrial Cavity Segmentation"],"prefix":"10.1145","author":[{"given":"Jingjing","family":"Xiao","sequence":"first","affiliation":[{"name":"Department of Medical Engineering, Xinqiao Hospital, Army Medical University, Chongqing, China"}]},{"given":"Dongyue","family":"Si","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China"}]},{"given":"Yanfang","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Xinqiao Hospital, Army Medical University, Chongqing, China"}]},{"given":"Jie","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China"}]},{"given":"Haiyan","family":"Ding","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.19102\/icrm.2017.081101"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Badger TJ Daccarett M Akoum NW etal Evaluation of left atrial lesions after initial and repeat atrial fibrillation ablation: lessons learned from delayed-enhancement MRI in repeat ablation procedures. Circ Arrhythm Electrophysiol. 2010;3(3):249--259. DOI=10.1161\/CIRCEP.109.868356  Badger TJ Daccarett M Akoum NW et al. Evaluation of left atrial lesions after initial and repeat atrial fibrillation ablation: lessons learned from delayed-enhancement MRI in repeat ablation procedures. Circ Arrhythm Electrophysiol. 2010;3(3):249--259. DOI=10.1161\/CIRCEP.109.868356","DOI":"10.1161\/CIRCEP.109.868356"},{"key":"e_1_3_2_1_3_1","volume-title":"Tailored management of atrial fibrillation using a LGE-MRI based model: from the clinic to the electrophysiology laboratory. J Cardiovasc Electrophysiol. 2011;22(4):481--187. DOI=10.1111\/j.1540-8167","author":"Vergara GR","year":"2010","unstructured":"Vergara GR , Marrouche NF. Tailored management of atrial fibrillation using a LGE-MRI based model: from the clinic to the electrophysiology laboratory. J Cardiovasc Electrophysiol. 2011;22(4):481--187. DOI=10.1111\/j.1540-8167 . 2010 . 01941.x Vergara GR, Marrouche NF. Tailored management of atrial fibrillation using a LGE-MRI based model: from the clinic to the electrophysiology laboratory. J Cardiovasc Electrophysiol. 2011;22(4):481--187. DOI=10.1111\/j.1540-8167. 2010. 01941.x"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2014.01.016"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Makropoulos A Counsell SJ Rueckert D. A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage. 2018; 170:231--248. DOI=10.1016\/j.neuroimage.2017.06.074  Makropoulos A Counsell SJ Rueckert D. A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage. 2018; 170:231--248. DOI=10.1016\/j.neuroimage.2017.06.074","DOI":"10.1016\/j.neuroimage.2017.06.074"},{"key":"e_1_3_2_1_6_1","unstructured":"Yue M Wei Q Deng W etal A Review of Automatic Liver Tumor Segmentation Based on Computed Tomography. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018;35(3):481--487. DOI=10.7507\/1001-5515.201708009  Yue M Wei Q Deng W et al. A Review of Automatic Liver Tumor Segmentation Based on Computed Tomography. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018;35(3):481--487. DOI=10.7507\/1001-5515.201708009"},{"key":"e_1_3_2_1_7_1","volume-title":"Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:1275--1278","author":"Nasr-Esfahani M","year":"2018","unstructured":"Nasr-Esfahani M , Mohrekesh M , Akbari M , Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:1275--1278 . DOI=10.1109\/EMBC. 2018 .8512536 Nasr-Esfahani M, Mohrekesh M, Akbari M, et al. Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network. Conf Proc IEEE Eng Med Biol Soc. 2018; 2018:1275--1278. DOI=10.1109\/EMBC.2018.8512536"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Tao Q Ipek EG Shahzad R etal Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment. J Magn Reson Imaging. 2016;44(2):346--354. DOI=10.1002\/jmri.25148  Tao Q Ipek EG Shahzad R et al. Fully automatic segmentation of left atrium and pulmonary veins in late gadolinium-enhanced MRI: Towards objective atrial scar assessment. J Magn Reson Imaging. 2016;44(2):346--354. DOI=10.1002\/jmri.25148","DOI":"10.1002\/jmri.25148"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Veni G Elhabian SY Whitaker RT. ShapeCut: Bayesian surface estimation using shape-driven graph. Med Image Anal. 2017; 40:11--29. DOI=10.1016\/j.media.2017.04.005  Veni G Elhabian SY Whitaker RT. ShapeCut: Bayesian surface estimation using shape-driven graph. Med Image Anal. 2017; 40:11--29. DOI=10.1016\/j.media.2017.04.005","DOI":"10.1016\/j.media.2017.04.005"},{"key":"e_1_3_2_1_10_1","volume-title":"Atrial scar quantification via multi-scale CNN in the graph-cuts framework [published online ahead of print","author":"Li L","year":"2019","unstructured":"Li L , Wu F , Yang G , Atrial scar quantification via multi-scale CNN in the graph-cuts framework [published online ahead of print , 2019 Nov 16]. Med Image Anal . 2019; 60:101595. DOI=10.1016\/j.media.2019.101595 Li L, Wu F, Yang G, et al. Atrial scar quantification via multi-scale CNN in the graph-cuts framework [published online ahead of print, 2019 Nov 16]. Med Image Anal. 2019; 60:101595. DOI=10.1016\/j.media.2019.101595"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2866845"},{"key":"e_1_3_2_1_12_1","unstructured":"https:\/\/www.datasciencecentral.com\/forum\/topics\/2018-atrial-segmentation-challenge  https:\/\/www.datasciencecentral.com\/forum\/topics\/2018-atrial-segmentation-challenge"},{"issue":"2","key":"e_1_3_2_1_13_1","first-page":"325","article-title":"A review on machine learning principles for multi-view biological data integration","volume":"19","author":"Yifeng Li","year":"2018","unstructured":"Yifeng Li , Fang-Xiang Wu, Alioune Ngom , A review on machine learning principles for multi-view biological data integration , Briefings in Bioinformatics , Volume 19 , Issue 2 , March 2018 , Pages 325 -- 340 , DOI=10.1093\/bib\/bbw113 Yifeng Li, Fang-Xiang Wu, Alioune Ngom, A review on machine learning principles for multi-view biological data integration, Briefings in Bioinformatics, Volume 19, Issue 2, March 2018, Pages 325--340, DOI=10.1093\/bib\/bbw113","journal-title":"Briefings in Bioinformatics"},{"key":"e_1_3_2_1_14_1","first-page":"565","article-title":"Fully convolutional neural networks for volumetric medical image segmentation","volume":"3","author":"Milletari F","year":"2016","unstructured":"Milletari F , Navab N , Ahmadi S A . V-net : Fully convolutional neural networks for volumetric medical image segmentation , International Conference on 3D Vision, 2016 : 565 -- 571 . DOI= 10.1109\/3DV.2016.79 Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation, International Conference on 3D Vision, 2016: 565--571. DOI= 10.1109\/3DV.2016.79","journal-title":"International Conference on"},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019:  605--613","author":"Yu L","unstructured":"Yu L , Wang S , Li X , Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation , International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019: 605--613 . DOI= https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67 10.1007\/978-3-030-32245-8_67 Yu L, Wang S, Li X, et al. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019: 605--613. DOI= https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"},{"key":"e_1_3_2_1_16_1","unstructured":"https:\/\/pypi.org\/project\/MedPy\/0.1.0\/.  https:\/\/pypi.org\/project\/MedPy\/0.1.0\/."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12832"}],"event":{"name":"AIPR 2020: 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition","location":"Xiamen China","acronym":"AIPR 2020"},"container-title":["Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3430199.3430203","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3430199.3430203","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3430199.3430203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:13Z","timestamp":1750191433000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3430199.3430203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,26]]},"references-count":17,"alternative-id":["10.1145\/3430199.3430203","10.1145\/3430199"],"URL":"https:\/\/doi.org\/10.1145\/3430199.3430203","relation":{},"subject":[],"published":{"date-parts":[[2020,6,26]]},"assertion":[{"value":"2020-12-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}