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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\u2010enhanced (<jats:styled-content style=\"fixed-case\">LGE<\/jats:styled-content>) magnetic resonance imaging (<jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content>) 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 <jats:styled-content style=\"fixed-case\">AF<\/jats:styled-content> is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content> Roadmap data) and of scar tissue within the heart (from <jats:styled-content style=\"fixed-case\">LGE MRI<\/jats:styled-content> data) acquired in patients with <jats:styled-content style=\"fixed-case\">AF<\/jats:styled-content>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our fully automatic pipeline uniquely combines: (a) a multiatlas\u2010based whole heart segmentation (<jats:styled-content style=\"fixed-case\">MA<\/jats:styled-content>\u2010<jats:styled-content style=\"fixed-case\">WHS<\/jats:styled-content>) to determine the cardiac anatomy from an <jats:styled-content style=\"fixed-case\">MRI<\/jats:styled-content> Roadmap acquisition which is then mapped to <jats:styled-content style=\"fixed-case\">LGE MRI<\/jats:styled-content>, and (b) a super\u2010pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in <jats:styled-content style=\"fixed-case\">LGE MRI<\/jats:styled-content>. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long\u2010standing <jats:styled-content style=\"fixed-case\">AF<\/jats:styled-content>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Both our <jats:styled-content style=\"fixed-case\">MA<\/jats:styled-content>\u2010<jats:styled-content style=\"fixed-case\">WHS<\/jats:styled-content> 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow <jats:styled-content style=\"fixed-case\">LGE MRI<\/jats:styled-content> to be used as an objective assessment tool for localization, visualization, and quantitation of atrial scarring and to guide ablation treatment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.12832","type":"journal-article","created":{"date-parts":[[2018,2,26]],"date-time":"2018-02-26T15:46:53Z","timestamp":1519660013000},"page":"1562-1576","source":"Crossref","is-referenced-by-count":44,"title":["Fully automatic segmentation and objective assessment of atrial scars for long\u2010standing persistent atrial fibrillation patients using late gadolinium\u2010enhanced <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"45","author":[{"given":"Guang","family":"Yang","sequence":"first","affiliation":[{"name":"Cardiovascular Research Centre Royal Brompton Hospital London SW3 6NP UK"},{"name":"National Heart and Lung Institute Imperial College London London SW7 2AZ 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