Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceTo develop an AI system for precise and fully automated, simultaneous segmentation of the liver and spleen from T1-weighted and T2-weighted MRI data. Our study compares the performance of the U-Net Transformer (UNETR) and the standard 3D U-Net model for simultaneous liver and spleen segmentation using MRI images from pediatric and adult patients from multiple institutions. Our work demonstrates that the UNETR shows a statistical improvement over 3D U-Net in both liver and spleen segmentations.
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