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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