Authors - Manar Eloued , Narjes Benameur , Sonia Esseghaier, Salam Labidi Abstract - Magnetic Resonance Elastography (MRE) is a novel, non-invasive im-aging technique for assessing liver stiffness. However, the lack of standardization introduces variability in measurements. The Manual selection of the Region of Interest (ROI) remains subjective and operator-dependent, often including areas with blood vessels or poor wave propagation, which can compromise measurement accuracy. This study proposes a deep learning- based approach to automatically identify an optimal region for liver stiffness measurement (LSM). A total of 160 MRE ex-ams, comprising paired magnitude and wave attenuation images from both healthy individuals and patients with liver disease, were used. A 3D U-Net architecture was trained to segment the liver, blood vessels, gallbladder, and biliary ducts from magnitude images, as well as regions of good wave propagation from attenuation images. The final ROI was obtained by intersecting these segmented regions. The model performance was evaluated on a separate test set using the Dice Similarity Coefficient (DSC), paired Student’s t-test, and Bland-Altman analysis. The resulting LSM region achieved a DSC of 0.89. The t-test yielded p = 0.68, indicating no significant difference between the automated and manual ROIs (p > 0.05). This automated pipeline reduced analysis time from approximately 20 minutes manually to less than 10 seconds automatically, while ensuring reproducibility and reducing operator dependency in MRE by standardizing ROI selection while maintaining diagnostic accuracy. It offers a promising solution to improve the reliability of LSM, particularly for longitudinal follow-up.
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