Attention-Driven Semantic Segmentation of Liver Parenchyma in Ultrasound Imaging with Application to Progressive NASH Risk Stratification

Nonalcoholic fatty liver disease represents a major global burden of liver-related illnesses. Its progressive form, known as nonalcoholic steatohepatitis (Progressive NASH), is closely associated with moderate to advanced liver fibrosis. The FibroScan-AST score has been proposed as a non-invasive assessment tool to effectively identify high-risk patients who meet the criteria for progressive NASH. However, as the FAST score relies on blood test data, its applicability for real-time risk prediction remains limited.This study focuses on the high-precision segmentation of liver parenchyma in ultrasound images and proposes a modified U-Net architecture integrated with channel and spatial attention modules. The model is designed to accurately extract the liver parenchyma region while mitigating interference from rib shadows, intestinal gas, and background noise. To verify the practical applicability of the segmentation model, the FibroScan-AST classification task was incorporated. A weighted Dice loss was employed to address the class imbalance caused by the relatively small target area, and various data augmentation strategies were utilized to enhance generalization. The segmentation model achieved an average Dice score of 0.9 on the test set, demonstrating high accuracy in delineating liver parenchyma. Visual inspection further confirms that the predicted liver boundaries are clearer, with non-liver tissue effectively excluded, resulting in clean and semantically meaningful inputs for the downstream classification model.To validate the utility of the segmentation results, we further constructed a FAST classification model and compared the performance of three strategies: using raw images, using segmented ROIs, and an end-to-end integration of segmentation and classification. Experimental results show that using the segmented ROI as input significantly improves the F1-score (from 0.47 to 0.61), confirming that accurate liver segmentation can enhance downstream task performance and hold strong clinical application potential. This study verifies the necessity of high-quality liver parenchyma segmentation in ultrasound imaging and provides an image-driven framework for progressive NASH screening.