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Abstract #0518

Automated liver-segment localization and segmental hepatic proton-density fat fraction measurement

Kang Wang1, Adrija Mamidipalli1, Kevin William Blansit2, Jonathan Charles Hooker1, Claude B. Sirlin1, and Albert Hsiao1

1Radiology, University of California, San Diego, La Jolla, CA, United States, 2Biomedical Informatic, University of California, San Diego, La Jolla, CA, United States

Hepatic proton-density fat fraction (PDFF) has emerged as an imaging biomarker for liver fat content in non-alcoholic fatty liver disease. To account for the spatial heterogeneity of liver fat, one approach is to manually place a region of interest (ROI) and estimate the PDFF in each hepatic Couinaud segment separately. We trained a convolutional neural network to automatically locate each liver segment and calculate segmental PDFF values. We show that segmental PDFF measurement based on our automated approach closely matches those based on manual placement by a trained image analyst, demonstrating the feasibility of utilizing CNNs to automatically extract clinically valuable quantitative information from source images.

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