Abstract
BACKGROUND: Liver fibrosis is a pathological outcome of chronic liver injury and a hallmark of multiple chronic liver diseases. Magnetic resonance elastography (MRE) provides a non-invasive modality for evaluating the severity of liver fibrosis. OBJECTIVE: This study aimed to develop and evaluate deep learning-based segmentation models for the automated assessment of liver fibrosis using MRE images, with a focus on comparing the performance of a conventional U-Net model and a UNet-ResNet50-32 × 4d architecture model. METHODS: A retrospective analysis was conducted on 319 patients enrolled between January 2018 and December 2020. MRE images were processed and segmented using two U-Net-based models. Model performance was assessed through correlation coefficients, intersection over union (IoU), and additional segmentation metrics. RESULTS: The UNet-ResNet50-32 × 4d model demonstrated strong agreement with ground truth annotations, achieving correlation coefficients of 0.952 in the training phase and 0.943 in the validation phase, along with an Dice score of 85.68%, confirming its high segmentation accuracy. CONCLUSIONS: The UNet-ResNet50-32 × 4d model exhibited robust performance and may serve as a reliable tool for the rapid and accurate assessment of liver fibrosis severity. The integration of automated segmentation into MRE analysis has the potential to improve clinical workflows and support timely decision-making in the management of chronic liver disease.