Automatic segmentation of left ventricular myocardium on LGE images of patients with chronic myocardial infarction is challenging due to inhomogeneous signal intensities in the presence of myocardial scar. However, the inclusion of scar in automatically generated myocardium segmentations is critical for further LGE analysis. We propose a Deep Learning-based method for myocardial segmentation on cardiac LGE images, achieving average Dice scores of 0.78 and 0.80 on test images with and without scar, respectively. We furthermore evaluate the network’s sensitivity to scar and show that it can be improved by incorporating synthetic images with diverse enhancement patterns in the training data.
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