2021
DOI: 10.1016/j.media.2020.101832
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A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging

Abstract: This is a repository copy of A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging.

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Cited by 245 publications
(170 citation statements)
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References 39 publications
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“…The patient-specific dataset was obtained from two sources. The first dataset, from the Atrial Segmentation Challenge at the Statistical Atlases and Computational Modelling of the Heart 2018 workshop (Xiong et al, 2021 ), consisted of 86 LGE-MRI scans from patients with AF (original resolution of 0.625 fnins-15-654170-i0001 0.625 fnins-15-654170-i0001 0.625 mm 3 ), and included the corresponding 3D left atria (LA) segmentations. The second dataset was collected at St Thomas' Hospital (Chubb et al, 2018 ) from 18 AF patients pre- and post-CA, providing total 36 LGE-MR images (original resolution of 1.3 fnins-15-654170-i0001 1.3 fnins-15-654170-i0001 4 mm 3 , reconstructed to 0.94 fnins-15-654170-i0001 0.94 fnins-15-654170-i0001 2 mm 3 ).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The patient-specific dataset was obtained from two sources. The first dataset, from the Atrial Segmentation Challenge at the Statistical Atlases and Computational Modelling of the Heart 2018 workshop (Xiong et al, 2021 ), consisted of 86 LGE-MRI scans from patients with AF (original resolution of 0.625 fnins-15-654170-i0001 0.625 fnins-15-654170-i0001 0.625 mm 3 ), and included the corresponding 3D left atria (LA) segmentations. The second dataset was collected at St Thomas' Hospital (Chubb et al, 2018 ) from 18 AF patients pre- and post-CA, providing total 36 LGE-MR images (original resolution of 1.3 fnins-15-654170-i0001 1.3 fnins-15-654170-i0001 4 mm 3 , reconstructed to 0.94 fnins-15-654170-i0001 0.94 fnins-15-654170-i0001 2 mm 3 ).…”
Section: Methodsmentioning
confidence: 99%
“…To prove its effectiveness and provide proof-of-concept results for patients, we will apply this approach to patient-specific 2D atria obtained by unfolding of 3D atrial datasets from AF patients. The latter have been obtained using late gadolinium enhancement-magnetic resonance imaging (LGE-MRI) (Williams et al, 2017 ; Xiong et al, 2021 ), which is primarily used to image cardiac fibrosis. LGE-MRI scans are routinely performed before CA procedures in many clinical centres, and hence, LGE data represents a perfect reference for studies of patient-specific AF scenarios and ablation patterns.…”
Section: Introductionmentioning
confidence: 99%
“…Other, fully automated machine learning or atlas-based approaches have been developed and implemented. However, their usability in an ECGI setting has not been fully explored (Xiong et al, 2021). Specific segmentation software include many opensource and commercial tools such as Seg3d, Slicer, ITK-SNAP, ImageJ, and others.…”
Section: Defining the Personalised Torso-heart Electromagnetic Relationshipmentioning
confidence: 99%
“…Based on experimental/clinical data on medical imaging and invasively acquired electroanatomic maps, atrial geometry with wall thickness [117], fibrosis distribution [118,119], myofibre orientation, regional electrical heterogeneities and AF driver distribution [120] were used to develop patient-specific 3D models [121]. In details, models with real atrial geometry are reconstructed from medical imaging, specifically from cardiac MRI and/or cardiac CT scans using image segmentation and 3D reconstruction algorithms [122][123][124][125]. In the 3D atria, fibrosis can be detected on late gadolinium enhancement MRI (LGE-MRI) using different thresholding techniques [126,127].…”
Section: Geometric and Image-based Atrial Modelingmentioning
confidence: 99%