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Abstract #3478

Automatic segmentation for liver fat quantification

Elisabeth Sarah Pickles1,2, Alexandre Bagur1,2, Ged Ridgway2, Benjamin Irving2, Daniel Bulte1, and Michael Brady2,3
1Institute of Biomedical Engineering, Oxford University, Oxford, United Kingdom, 2Perspectum Diagnostics, Oxford, United Kingdom, 3Department of Oncology, Oxford University, Oxford, United Kingdom

By segmenting the liver on an MRI Proton Density Fat Fraction (PDFF) map a median PDFF value is obtained, indicating the amount of fat in the liver. Automatic segmentation is desirable, as manual segmentation is time consuming. We investigated a direct PDFF automatic segmentation method using a U-Net model and compared it to a T1-based PDFF segmentation. We show that the median values obtained are comparable, and the Dice scores are relatively good, although not as high as desired. Visually the direct PDFF segmentation is not always optimal. We suggest that improvement of the model is desirable.

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