A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
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Published:2021-01
Issue:
Volume:67
Page:101832
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ISSN:1361-8415
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Container-title:Medical Image Analysis
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language:en
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Short-container-title:Medical Image Analysis
Author:
Xiong Zhaohan, Xia Qing, Hu Zhiqiang, Huang Ning, Bian Cheng, Zheng Yefeng, Vesal Sulaiman, Ravikumar Nishant, Maier Andreas, Yang Xin, Heng Pheng-AnnORCID, Ni Dong, Li CaiziORCID, Tong Qianqian, Si WeixinORCID, Puybareau ElodieORCID, Khoudli Younes, Géraud Thierry, Chen Chen, Bai Wenjia, Rueckert Daniel, Xu Lingchao, Zhuang XiahaiORCID, Luo XinzheORCID, Jia Shuman, Sermesant Maxime, Liu YashuORCID, Wang Kuanquan, Borra DavideORCID, Masci AlessandroORCID, Corsi Cristiana, de Vente Coen, Veta Mitko, Karim Rashed, Preetha Chandrakanth Jayachandran, Engelhardt Sandy, Qiao Menyun, Wang Yuanyuan, Tao Qian, Nuñez-Garcia Marta, Camara Oscar, Savioli Nicolo, Lamata Pablo, Zhao Jichao
Funder
Health Research Council of New Zealand
Subject
Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology
Reference47 articles.
1. Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved;Bernard;IEEE Trans. Med. Imaging,2018 2. Pyramid network with online hard example mining for accurate left atrium segmentation;Bian,2018 3. A semantic-wise convolutional neural network approach for 3-D left atrium segmentation from late gadolinium enhanced magnetic resonance imaging;Borra,2018 4. Multi-task learning for left atrial segmentation on GE-MRI;Chen,2018 5. Encoder-decoder with atrous separable convolution for semantic image segmentation;Chen,2018
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