Abstract #3559

Automated Segmentation of Late Gadolinium Enhanced CMR with Deep Learning

Daming Shen^1,2, Justin J Baraboo^1,2, Brandon C Benefield³, Daniel C Lee^2,3, Michael Markl^1,2, and Daniel Kim^1,2

¹Biomedical Engineering, Northwestern University, Evanston, IL, United States, ²Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, ³Feinberg Cardiovascular Research Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, United States

Late gadolinium enhanced (LGE) CMR is the gold standard test for assessment of myocardial scarring. While quantifying scar volume is helpful to clinical decision making, its lengthy image segmentation time limits its use in practice. The purpose of this study is to enable fully automated LGE image segmentation using deep learning (DL) and explore a more efficient way of using annotation.

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