Automated MRI liver segmentation enables the inline evaluation of parametric maps for iron quantification with improved accuracy, efficiency, and repeatability compared to manual efforts. Existing methods optimized for adults and normal livers do not perform well on challenging cases in children and patients with iron overload. We developed a deep learning-based solution trained on 861 T1-weighted MRI that provided significantly improved liver segmentation compared to a commercially available solution and demonstrated its robustness on a challenging cohort of pediatric patients including cases with high iron content.
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