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Abstract #5659

Fully Automated Left Ventricle Scar Quantification with Deep Learning

Johannes Rausch1, Bjoern Menze1, Raymond H Chan2, Jihye Jang1,3, Evan Appelbaum3, and Reza Nezafat3

1Technical University of Munich, Munich, Germany, 2Toronto General Hospital, University Health Network, Toronto, Canada, 3Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States

We present a fully automated approach for quantification of left ventricle scar in Late Gadolinium Enhancement (LGE) cardiac MR (CMR), using a residual neural network. LGE images were acquired in 1075 patients with known hypertrophic cardiomyopathy in a multi-center clinical trial. Scar segmentation was performed in all patients by a CMR-trained cardiologist. For training, we use a two-phase procedure, using cropped and full-sized images consecutively. We train different models using sigmoid cross-entropy loss and Dice loss and measure average LV segmentation Dice scores of 0.77 ± 0.10 and 0.70 ± 0.12 and estimated scar percentage mismatches of 3.59% and 3.00%, respectively.

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