Automated multi-frame U-Net segmentation of epicardial adipose tissue achieved a Dice score of 0.77, performing equivalently to inter-observer bias and outperforming FCNB (P<0.0001).
Does deep-learning segmentation accurately quantify epicardial adipose tissue area on four-chamber cardiac MRI compared to manual contouring in healthy, obese, and diabetic patients?
A multi-frame U-Net deep learning model can accurately and automatically quantify epicardial adipose tissue area on standard four-chamber cardiac MRI, performing comparably to manual contouring.
Absolute Event Rate: 0.77% vs 0.76%
p-value: p=<0.0001
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.
Daudé et al. (Thu,) conducted a other in Epicardial adipose tissue overload (n=100). Multi-frame U-Net deep-learning segmentation vs. FCNB and manual contouring (inter-observer bias) was evaluated on Segmentation performance (Dice score) (p=<0.0001). Automated multi-frame U-Net segmentation of epicardial adipose tissue achieved a Dice score of 0.77, performing equivalently to inter-observer bias and outperforming FCNB (P<0.0001).