Zobrazeno 1 - 10
of 729
pro vyhledávání: '"Mohiaddin, Raad"'
Autor:
Chen, Jun, Zhang, Heye, Mohiaddin, Raad, Wong, Tom, Firmin, David, Keegan, Jennifer, Yang, Guang
Semi-supervised learning provides great significance in left atrium (LA) segmentation model learning with insufficient labelled data. Generalising semi-supervised learning to cross-domain data is of high importance to further improve model robustness
Externí odkaz:
http://arxiv.org/abs/2109.08311
Autor:
Yahdev, Rashmi, Rahman-Halley, Shelley, Wong, Joyce, Opel, Aaisha, Kaba, Riyaz, Nyktari, Eva, Cambronero-Cortinas, Esther, Izgi, Cemil, Fairbairn, Timothy, Benton, Joanne, Chester, Ruth, Cunliffe, Eliane, Edmondson, Lucy, Gill, Matthew, Griffiths, Vicky, Harman, Rebecca, Huggett, Catherine, Keegan, Jenny, Kirby, Kevin, Lascelles, Karen, Manivarmane, Ramasamy, Munteanu, Iulia, O’Brien, Karen, Phyl, Tess, Rahneva, Tsveta, Riley, Cheryl, Rogers, Paula, Smith, Katherine, Wage, Rick, West, Cathy, Yakupoglu, Yakup, Yang, Guang, Audraite, Audra, Belchambers, Sandra, Hughes, Susan, Morgan, Maureen, Ronayne, Christina, Shaw, Rob, Simkus, Paulinus, Taylerson, Clive, Bruce, Chloe, McGregor, Andrew, Thomson, Catherine, Granville, Hollie, Snell, Alice, John, Sophia, Monteiro, Christiana, Augustine, Grace, Sutton, Nichola, Boyalla, Vennela, Haldar, Shouvik, Khan, Habib, Kralj-Hans, Ines, Banya, Winston, Lord, Joanne, Satishkumar, Anitha, Bahrami, Toufan, De Souza, Anthony, Clague, Jonathan R., Francis, Darrel P., Hussain, Wajid, Jarman, Julian W., Jones, David G., Chen, Zhong, Mediratta, Neeraj, Hyde, Jonathan, Lewis, Michael, Mohiaddin, Raad, Salukhe, Tushar V., Markides, Vias, McCready, James, Gupta, Dhiraj, Wong, Tom
Publikováno v:
In Heart Rhythm September 2024 21(9):1562-1569
Autor:
Chen, Jun, Yang, Guang, Khan, Habib, Zhang, Heye, Zhang, Yanping, Zhao, Shu, Mohiaddin, Raad, Wong, Tom, Firmin, David, Keegan, Jennifer
Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on
Externí odkaz:
http://arxiv.org/abs/2105.00234
Autor:
Yang, Guang, Chen, Jun, Gao, Zhifan, Li, Shuo, Ni, Hao, Angelini, Elsa, Wong, Tom, Mohiaddin, Raad, Nyktari, Eva, Wage, Ricardo, Xu, Lei, Zhang, Yanping, Du, Xiuquan, Zhang, Heye, Firmin, David, Keegan, Jennifer
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment suc
Externí odkaz:
http://arxiv.org/abs/2002.00440
Autor:
Chen, Jun, Zhang, Heye, Zhang, Yanping, Zhao, Shu, Mohiaddin, Raad, Wong, Tom, Firmin, David, Yang, Guang, Keegan, Jennifer
Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it does not always hold that the manually labeled data and the unlabeled da
Externí odkaz:
http://arxiv.org/abs/1907.10267
Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge
Autor:
Zhuang, Xiahai, Li, Lei, Payer, Christian, Stern, Darko, Urschler, Martin, Heinrich, Mattias P., Oster, Julien, Wang, Chunliang, Smedby, Orjan, Bian, Cheng, Yang, Xin, Heng, Pheng-Ann, Mortazi, Aliasghar, Bagci, Ulas, Yang, Guanyu, Sun, Chenchen, Galisot, Gaetan, Ramel, Jean-Yves, Brouard, Thierry, Tong, Qianqian, Si, Weixin, Liao, Xiangyun, Zeng, Guodong, Shi, Zenglin, Zheng, Guoyan, Wang, Chengjia, MacGillivray, Tom, Newby, David, Rhode, Kawal, Ourselin, Sebastien, Mohiaddin, Raad, Keegan, Jennifer, Firmin, David, Yang, Guang
Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart.
Externí odkaz:
http://arxiv.org/abs/1902.07880
Autor:
Li, Lei, Wu, Fuping, Yang, Guang, Xu, Lingchao, Wong, Tom, Mohiaddin, Raad, Firmin, David, Keegan, Jennifer, Zhuang, Xiahai
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to
Externí odkaz:
http://arxiv.org/abs/1902.07877
Autor:
Li, Lei, Wu, Fuping, Yang, Guang, Wong, Tom, Mohiaddin, Raad, Firmin, David, Keegan, Jenny, Xu, Lingchao, Zhuang, Xiahai
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging
Externí odkaz:
http://arxiv.org/abs/1810.09123
Autor:
Wu, Fuping, Li, Lei, Yang, Guang, Wong, Tom, Mohiaddin, Raad, Firmin, David, Keegan, Jennifer, Xu, Lingchao, Zhuang, Xiahai
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition s
Externí odkaz:
http://arxiv.org/abs/1810.09075
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