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

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• dc.contributor.author Xiong, Zhaohan
• dc.contributor.author Xia, Qing
• dc.contributor.author Hu, Zhiqiang
• dc.contributor.author Huang, Ning
• dc.contributor.author Bian, Cheng
• dc.contributor.author Zheng, Yefeng
• dc.contributor.author Vesal, Sulaiman
• dc.contributor.author Ravikumar, Nishant
• dc.contributor.author Maier, Andreas
• dc.contributor.author Yang, Xin
• dc.contributor.author Heng, Pheng-Ann
• dc.contributor.author Ni, Dong
• dc.contributor.author Li, Caizi
• dc.contributor.author Tong, Qianqian
• dc.contributor.author Si, Weixin
• dc.contributor.author Puybareau, Elodie
• dc.contributor.author Khoudli, Younes
• dc.contributor.author Géraud, Thierry
• dc.contributor.author Chen, Chen
• dc.contributor.author Bai, Wenjia
• dc.contributor.author Rueckert, Daniel
• dc.contributor.author Xu, Lingchao
• dc.contributor.author Zhuang, Xiahai
• dc.contributor.author Luo, Xinzhe
• dc.contributor.author Jia, Shuman
• dc.contributor.author Sermesant, Maxime
• dc.contributor.author Liu, Yashu
• dc.contributor.author Wang, Kuanquan
• dc.contributor.author Borra, Davide
• dc.contributor.author Masci, Alessandro
• dc.contributor.author Corsi, Cristiana
• dc.contributor.author Vente, Coen de
• dc.contributor.author Veta, Mitko
• dc.contributor.author Karim, Rashed
• dc.contributor.author Jayachandran Preetha, Chandrakanth
• dc.contributor.author Engelhardt, Sandy
• dc.contributor.author Qiao, Menyun
• dc.contributor.author Wang, Yuanyuan
• dc.contributor.author Tao, Qian
• dc.contributor.author Nuñez-Garcia, Marta
• dc.contributor.author Camara, Oscar
• dc.contributor.author Savioli, Nicolo
• dc.contributor.author Lamata, Pablo
• dc.contributor.author Zhao, Jichao
• dc.date.accessioned 2023-02-22T07:25:37Z
• dc.date.available 2023-02-22T07:25:37Z
• dc.date.issued 2021
• dc.description.abstract Segmentation of medical images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) used for visualizing diseased atrial structures, is a crucial first step for ablation treatment of atrial fibrillation. However, direct segmentation of LGE-MRIs is challenging due to the varying intensities caused by contrast agents. Since most clinical studies have relied on manual, 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 atrial 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 that the top method achieved a Dice score of 93.2% and a mean surface to 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 superior results than traditional methods and machine learning approaches containing single CNNs. This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field. Furthermore, the findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging community.
• dc.format.mimetype application/pdf
• dc.identifier.citation Xiong Z, Xia Q, Hu Z, Huang N, Bian C, Zheng Y, Vesal S, Ravikumar N, Maier A, Yang X, Heng PA, Ni D, Li C, Tong Q, Si W, Puybareau E, Khoudli Y, Géraud T, Chen C, Bai W, Rueckert D, Xu L, Zhuang X, Luo X, Jia S, Sermesant M, Liu Y, Wang K, Borra D, Masci A, Corsi C, de Vente C, Veta M, Karim R, Jayachandran Preetha C, Engelhardt S, Qiao M, Wang Y, Tao Q, Nuñez-Garcia M, Camara O, Savioli N, Lamata P, Zhao J. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med Image Anal. 2021;67:101832. DOI: 10.1016/j.media.2020.101832
• dc.identifier.doi http://dx.doi.org/10.1016/j.media.2020.101832
• dc.identifier.issn 1361-8415
• dc.identifier.uri http://hdl.handle.net/10230/55847
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Medical Image Analysis. 2021;67:101832.
• dc.relation.isreferencedby https://ars.els-cdn.com/content/image/1-s2.0-S1361841520301961-mmc1.pdf
• dc.rights © Elsevier http://dx.doi.org/10.1016/j.media.2020.101832
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Left atrium
• dc.subject.keyword Convolutional neural networks
• dc.subject.keyword Late gadolinium-enhanced magnetic resonance imaging
• dc.subject.keyword Image segmentation
• dc.title A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion