A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced
Cardiac Magnetic Resonance Imaging
- URL: http://arxiv.org/abs/2004.12314v3
- Date: Thu, 7 May 2020 14:05:14 GMT
- Title: A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced
Cardiac Magnetic Resonance Imaging
- Authors: Zhaohan Xiong, Qing Xia, Zhiqiang Hu, Ning Huang, Cheng Bian, Yefeng
Zheng, Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Xin Yang, Pheng-Ann
Heng, Dong Ni, Caizi Li, Qianqian Tong, Weixin Si, Elodie Puybareau, Younes
Khoudli, Thierry Geraud, Chen Chen, Wenjia Bai, Daniel Rueckert, Lingchao Xu,
Xiahai Zhuang, Xinzhe Luo, Shuman Jia, Maxime Sermesant, Yashu Liu, Kuanquan
Wang, Davide Borra, Alessandro Masci, Cristiana Corsi, Coen de Vente, Mitko
Veta, Rashed Karim, Chandrakanth Jayachandran Preetha, Sandy Engelhardt,
Menyun Qiao, Yuanyuan Wang, Qian Tao, Marta Nunez-Garcia, Oscar Camara,
Nicolo Savioli, Pablo Lamata, Jichao Zhao
- Abstract summary: " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset.
Analyse of the submitted algorithms using technical and biological metrics was performed.
Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
- Score: 90.29017019187282
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 this, we
organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs,
currently the world's largest cardiac LGE-MRI dataset, and associated labels of
the left atrium segmented by three medical experts, ultimately attracting the
participation of 27 international teams. In this paper, extensive analysis of
the submitted algorithms using technical and biological metrics was performed
by undergoing subgroup analysis and conducting hyper-parameter analysis,
offering an overall picture of the major design choices of convolutional neural
networks (CNNs) and practical considerations for achieving state-of-the-art
left atrium segmentation. Results show the top method achieved a dice score of
93.2% and a mean surface to a surface distance of 0.7 mm, significantly
outperforming prior state-of-the-art. Particularly, our analysis demonstrated
that double, sequentially used CNNs, in which a first CNN is used for automatic
region-of-interest localization and a subsequent CNN is used for refined
regional segmentation, achieved far superior results than traditional methods
and pipelines containing single CNNs. This large-scale benchmarking study makes
a significant step towards much-improved segmentation methods for cardiac
LGE-MRIs, and will serve as an important benchmark for evaluating and comparing
the future works in the field.
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