Abstract #2876

Generating Cardiac Segmentation Masks of Real-Time Images from Self-Gated MRI to Train Neural Networks

Martin Schilling¹, Sebastian Rosenzweig^1,2, Moritz Blumenthal¹, and Martin Uecker^1,2,3

¹Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany, ²DZHK (German Centre for Cardiovascular Research), Göttingen, Germany, ³Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany

Cardiac segmentation is essential for analyzing cardiac function. Manual labeling is relatively slow, so machine learning methods have been proposed to increase segmentation speed and precision. These methods typically rely on cine MR images and supervised learning. However, for real-time cardiac MRI, ground truth segmentations are difficult to obtain due to lower image quality compared to cine MRI. Here, we present a method to obtain ground truth segmentation for real-time images on the basis of self-gated MRI (SSA-FARY).

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