Advances in liver, liver lesion, hepatic vasculature, and biliary segmentation: a comprehensive review of traditional and deep learning approaches

Background and motivation

Liver segmentation plays a critical role in medical imaging, aiding in diagnosis, treatment planning, and surgical interventions for liver diseases. Precise segmentation of liver structures, including vessels, tumors, and other substructures, is essential for effective patient management. Traditional manual methods are time-consuming and prone to variability, prompting the development of automated techniques. This review aims to evaluate the evolution of liver segmentation methodologies, focusing on recent advancements in deep learning and hybrid approaches.

Materials and methods

This review follows the PRISMA guidelines for systematic analysis, including a detailed database search across PubMed, Web of Science, Scopus, and IEEE Xplore. The search focused on segmentation techniques for various liver structures using deep learning, traditional methods, and hybrid models. A total of 7819 studies were initially identified, with 190 selected for detailed analysis based on inclusion criteria like Dice Similarity Coefficient (DSC) metrics and clinical applicability.

Results

The analysis identified deep learning models, such as U-Net variants and Swin Transformer-based architectures, as leading methods for liver parenchyma and tumor segmentation, achieving DSC values up to 98.9% on benchmark datasets. For vessel segmentation, methods like DeepLabV3+ and the feature-based approaches demonstrated robustness across different datasets. Despite progress, challenges remain in segmenting structures like biliary ducts and hematomas due to limited annotated data and imaging variability.

Discussion

While deep learning has significantly improved segmentation accuracy, challenges such as class imbalance and variability across imaging modalities persist. Hybrid approaches that combine traditional image processing with advanced neural networks show potential for further improvement. Future research should focus on enhancing generalizability through multi-modal data integration and exploring semi-supervised learning methods to overcome data scarcity.

Conclusion

This comprehensive review highlights the advancements and ongoing challenges in liver segmentation, emphasizing the need for continuous innovation. By addressing current limitations, future methodologies can improve accuracy, efficiency, and clinical relevance, ultimately enhancing patient outcomes in hepatology.