Magnetic Resonance Elastography (MRE) accurately predicts fibrosis by measuring liver stiffness. The subjectivity in human analysis poses the biggest challenge to stiffness measurement reproducibility, and also complicates the training of a neural network to automate the task. In this work, we present a CNN-based stiffness measurement tool, giving special attention to training and validation in context of reader subjectivity. Compared to an older automated tool used by our institution in a reader-verified workflow, the CNN reduces ROI failure rate by 50%, and has an excellent agreement in measured stiffness with reader-verified target ROIs.
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