• Corpus ID: 216553758

A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging

@article{Xiong2020AGB,
  title={A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging},
  author={Zhaohan Xiong and Qing Xia and Zhiqiang Hu and Ning Huang and Sulaiman Vesal and Nishant Ravikumar and Andreas K. Maier and Caizi Li and Qianqian Tong and Weixin Si and {\'E}lodie Puybareau and Younes Khoudli and Thierry G{\'e}raud and Chen Chen and Wenjia Bai and Daniel Rueckert and Lingchao Xu and Xiahai Zhuang and Xinzhe Luo and Shuman Jia and Maxime Sermesant and Davide Borra and Alessandro Masci and Cristiana Corsi and Rashed Karim and Coen de Vente and Mitko Veta and Chandrakanth Jayachandran Preetha and Sandy Engelhardt and Menyun Qiao and Yuanyuan Wang and Qian Tao and Marta Nu{\~n}ez-Garcia and Oscar Camara and Yashu Liu and Kuanquan Wang and Nicol{\'o} Savioli and Pablo Lamata and Jichao Zhao},
  journal={ArXiv},
  year={2020},
  volume={abs/2004.12314}
}
Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) widely used for visualizing diseased cardiac structures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation of LGE-MRIs is challenging due to its attenuated contrast. Since most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning approaches. To address… 

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