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Abstract #1014

Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images

Marc Vornehm1,2, Maximilian Fenski3, Elisabeth Preuhs4, Andreas Maier4, Jeanette Schulz-Menger3, Jens Wetzl1, and Daniel Giese1,5
1Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany, 2Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 3Working Group Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, Charité Medical Faculty, Max Delbrück Center for Molecular Medicine, HELIOS Klinikum Berlin Buch, Department of Cardiology and Nephrology, Berlin, Germany, 4Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 5Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany


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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