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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