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Abstract #2234

Deep Learning based Semantic Segmentation of Scar Tissue in Late-Gadolinium Enhancement CMR – First Results

Julius Frederik Heidenreich1, Tobias Gassenmaier1, Markus Ankenbrand2, David Lohr2, Thorsten Alexander Bley1, and Tobias Wech1
1Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany, 2Congestive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany

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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