Quantitative analysis of scar tissue in late gadolinium enhancement (LGE) cardiac magnetic resonance imaging (CMR) typically requires manual or at best semi-automatic segmentation by a trained physician. To supersede this time-consuming and tedious task, a convolutional neural network with a U-Net architecture and a ResNet34 backbone was trained for semantic segmentation of scar tissue in LGE CMR. The predictions of the proposed model yielded high performance for the detection of focal scar tissue and bears thus potential for fully automated and consequently time-efficient post-processing.
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