A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

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

Publisher

Elsevier BV

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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