In this study, we present an automatic, multi-regional and multi-sequence deep-learning-based algorithm for background segmentation on both DSC and DCE images which consisted of a 2D U-net trained with a large multi-centric and multi-vendor database including DSC brain, DCE brain, DCE breast, DCE abdomen and DCE pelvis data. Cross-validation-based training results showed an overall good performance of the proposed algorithm with a median Dice score of 0.974 in test set and 0.979 over all datasets, and a median inference duration of 0.15s per volume on GPU. This is the first reported deep-learning-based multi-sequence and multi-regional background segmentation on MRI data.
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