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Abstract #1766

Improving U-Net based segmentation in cardiac parametrical T1 mapping by incorporating bounding box information

Darian Viezzer1,2, Thomas Hadler1,2, Edyta Blaszczyk1,2, Maximilian Fenski1,3, Jan Gröschel1,2, Steffen Lange4, and Jeanette Schulz-Menger1,2,3
1Charité Universitätsmedizin Berlin, Working Group on Cardiovascular Magnetic Resonance, Experimental and Clinical Research Center, a joint cooperation between the Charité Universitätsmedizin Berlin and the Max-Delbrück-Center for Molecular Medicine, Berlin, Germany, 2DZHK (German Centre for Cardiovascular Research), partner site Berlin, Berlin, Germany, 3Department of Cardiology and Nephrology, Helios Hospital Berlin-Buch, Berlin, Germany, 4Faculty for Computer Sciences, Hochschule Darmstadt (University of Applied Sciences), Darmstadt, Germany


Parametric mapping images contain to a large extent irrelevant background information. In order to hide some of it, we incorporated bounding box information into a U-net based segmentation network. Our dataset consisted of 845 training, 102 validation and 146 test T1 maps of native and post-contrast myocardium from different clinical studies, including healthy volunteers and patients with inflammatory heart disease, muscular dystrophies or chronic myocardial infarction. While cropping the image input improved the segmentation itself, a second input of the bounding box mask reduced the mean absolute and mean squared T1 deviation, which is clinically preferred.

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