Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI
Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI
Purpose: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. Methods: Our fully automatic pipeline uniquely combines: (a) a multiatlas-based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long-standing AF. Results: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. Conclusion: Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.
atrial fibrillation, cardiovascular magnetic resonance imaging, late gadolinium-enhanced MRI, medical image segmentation, whole heart segmentation
1562-1576
Yang, Guang
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Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Khan, Habib
ef5e6248-435c-40ff-97f2-1bf0a759c43e
Haldar, Shouvik
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Nyktari, Eva
8e081fa8-fa2c-424c-ae75-de6b8ae549d6
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Wage, Ricardo
af84c3d9-16c6-4d0d-b6bc-c1da7c1d18d5
Ye, Xujiong
a172909c-9f27-41b9-ab44-b09cef1fce50
Slabaugh, Greg
19f64c31-aa15-41b2-9084-e97c02b9b808
Mohiaddin, Raad
dd8235ec-41f2-4a54-bf22-16088df01765
Wong, Tom
d7ddec6a-c082-4ef2-b224-2c45a058b0fb
Keegan, Jennifer
6a9e3a51-99f0-430e-8891-bc9fb971a3a5
Firmin, David
9f62653f-537b-48e4-a102-47a352c1479e
1 April 2018
Yang, Guang
19f7479e-304e-40df-9504-bd3770ea3adf
Zhuang, Xiahai
c58e977b-e70e-4b37-9acd-b7f8070d98a8
Khan, Habib
ef5e6248-435c-40ff-97f2-1bf0a759c43e
Haldar, Shouvik
6cc78f9a-8489-4881-92fc-57b3c2d231f2
Nyktari, Eva
8e081fa8-fa2c-424c-ae75-de6b8ae549d6
Li, Lei
2da88502-0bd8-4e6b-8f7d-0c01a48b399e
Wage, Ricardo
af84c3d9-16c6-4d0d-b6bc-c1da7c1d18d5
Ye, Xujiong
a172909c-9f27-41b9-ab44-b09cef1fce50
Slabaugh, Greg
19f64c31-aa15-41b2-9084-e97c02b9b808
Mohiaddin, Raad
dd8235ec-41f2-4a54-bf22-16088df01765
Wong, Tom
d7ddec6a-c082-4ef2-b224-2c45a058b0fb
Keegan, Jennifer
6a9e3a51-99f0-430e-8891-bc9fb971a3a5
Firmin, David
9f62653f-537b-48e4-a102-47a352c1479e
Yang, Guang, Zhuang, Xiahai, Khan, Habib, Haldar, Shouvik, Nyktari, Eva, Li, Lei, Wage, Ricardo, Ye, Xujiong, Slabaugh, Greg, Mohiaddin, Raad, Wong, Tom, Keegan, Jennifer and Firmin, David
(2018)
Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI.
Medical Physics, 45 (4), .
(doi:10.1002/mp.12832).
Abstract
Purpose: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. Methods: Our fully automatic pipeline uniquely combines: (a) a multiatlas-based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long-standing AF. Results: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. Conclusion: Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.
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Accepted/In Press date: 17 February 2018
Published date: 1 April 2018
Additional Information:
Publisher Copyright:
© 2018 American Association of Physicists in Medicine
Keywords:
atrial fibrillation, cardiovascular magnetic resonance imaging, late gadolinium-enhanced MRI, medical image segmentation, whole heart segmentation
Identifiers
Local EPrints ID: 488645
URI: http://eprints.soton.ac.uk/id/eprint/488645
ISSN: 0094-2405
PURE UUID: 80062dda-3990-4734-9bcb-e3e95eca1941
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Date deposited: 27 Mar 2024 18:04
Last modified: 10 Apr 2024 02:14
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Contributors
Author:
Guang Yang
Author:
Xiahai Zhuang
Author:
Habib Khan
Author:
Shouvik Haldar
Author:
Eva Nyktari
Author:
Lei Li
Author:
Ricardo Wage
Author:
Xujiong Ye
Author:
Greg Slabaugh
Author:
Raad Mohiaddin
Author:
Tom Wong
Author:
Jennifer Keegan
Author:
David Firmin
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