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Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging

Abstract : In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects—comprising healthy, obese, and diabetic patients—who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (n = 80) and evaluated on an independent dataset (n = 20). Segmentation performances were compared to inter-intra observer bias with dice (DSC) and relative surface error (RSE). Both systole and diastole four-chamber area were correlated with total EAT volume (r = 0.77 and 0.74 respectively). Networks’ performances were equivalent to inter-observers’ bias (EAT: DSCInter = 0.76, DSCU-Net = 0.77, DSCFCNB = 0.76). U-net outperformed (p < 0.0001) FCNB on all metrics. Eventually, proposed multi-frame U-Net provided automated EAT area quantification with a 14.2% precision for the clinically relevant upper three quarters of EAT area range, scaling patients’ risk of EAT overload with 70% accuracy. Exploiting multi-frame U-Net in standard cine provided automated EAT quantification over a wide range of EAT quantities. The method is made available to the community through a FSLeyes plugin.
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https://hal-amu.archives-ouvertes.fr/hal-03516610
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Submitted on : Friday, January 7, 2022 - 1:41:19 PM
Last modification on : Friday, February 4, 2022 - 3:25:54 AM
Long-term archiving on: : Friday, April 8, 2022 - 7:20:20 PM

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Pierre Daudé, Patricia Ancel, Sylviane Confort Gouny, Alexis Jacquier, Frank Kober, et al.. Deep-Learning Segmentation of Epicardial Adipose Tissue Using Four-Chamber Cardiac Magnetic Resonance Imaging. Diagnostics, MDPI, 2022, 12 (1), pp.126. ⟨10.3390/diagnostics12010126⟩. ⟨hal-03516610⟩

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