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Abstract #4724

Fully Automated Liver Fat Assessment using Multi-Atlas Segmentation

Joel Kullberg1,2, Taro Langner1, Filip Malmberg1, Anders Hedström2, Jonathan Andersson1, Carl Sjöberg2, Lars Lind3, and Håkan Ahlström1

1Department of Radiology, Uppsala University, Uppsala, Sweden, 2Antaros Medical, BioVenture Hub, Mölndal, Sweden, 3Department of Medical Sciences, Uppsala University, Uppsala, Sweden

Non-alcoholic fatty liver disease (NAFLD) has become the most common liver disease with an estimated global prevalence of 25%. Its link to metabolic, cardiovascular, and more severe forms of liver disease presents a major challenge for future healthcare. MRI allows accurate quantification of liver fat concentration. Since manual delineation of the liver is time-consuming, measurements are typically performed in small subjectively selected regions of interest which limits accuracy and precision. This work presents an automated method for liver fat assessment using multi-atlas segmentation. Evaluation with measurements using manual liver segmentation (n=306) demonstrates excellent agreement (R=1.000, difference -0.03%, p=0.001).

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