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Abstract #4342

Simultaneous Liver and Spleen Segmentation using U-Net Transformer Model on T1-weighted and T2-weighted MRI Data

Huixian Zhang1, Redha Ali1, Hailong Li1, Wen Pan1, Scott B. Reeder2, David T. Harris2, William R. Masch3, Anum Alsam3, Krishna P. Shanbhogue4, Nehal A. Parikh1, Jonathan R. Dillman1, and Lili He1
1Cincinnati Children's Hospital, Cincinnati, OH, United States, 2University of Wisconsin-Madison, Madison, WI, United States, 3Michigan Medicine, University of Michigan, Ann Arbor, MI, United States, 4NYU Langone Health, New York, NY, United States

Synopsis

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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Keywords