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Abstract #2236

Fully automated quantification of left ventricular scar in patients with ischemic heart disease using deep learning and Gaussian mixture models

Cian Michael Scannell1, Adriana Villa1, Stefano Figliozzi1, Jack Lee1, Mikto Veta2, Marcel Breeuwer2,3, and Amedeo Chiribiri1
1King's College London, London, United Kingdom, 2Eindhoven University of Technology, Eindhoven, Netherlands, 3Philips Healthcare, Best, Netherlands

Late gadolinium enhancement MRI is crucial tool for guiding the management of patients with known/suspected myocardial infarction. Currently, clinical practice relies on the visual inspection of these images but there has been extensive work on semi-automated approaches for quantifying scar. Their utility is however limited by the time-intensive manual interactions involved, including the drawing of the myocardial contours. In this work, deep learning methodology is employed to automatically achieve the previously manual steps and these segmentations are used as input to a completely unsupervised scar quantification algorithm. This allows automatic, fast and reproducible quantification of regions of scar.

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