Deep Learning-Based Segmentation of Hepatic Structures: A Systematic Review of Model Architectures and Clinical Applications
DOI:
https://doi.org/10.29384/rbfm.2026.v20.19849001882Keywords:
Liver cancer, Hepatic segmentation, Deep learning, Artificial intelligence in healthcareAbstract
Liver cancer is a major public health problem and remains among the most incident neoplasms worldwide, with approximately 866,000 new cases reported in 2022, of which hepatocellular carcinoma accounts for the majority of diagnoses. Anatomical characterization of the liver and its lesions relies largely on computed tomography (CT) and magnetic resonance imaging (MRI), whose segmentation is still predominantly manual or semi-automatic. This process is time-consuming and may lead to variability between different observers or even for the same professional at different time points. This limitation has motivated the adoption of deep learning (DL)–based methods, particularly to improve the accuracy and reproducibility of hepatic segmentation. This review analyzed 16 studies selected based on predefined keywords and inclusion criteria, aiming to identify methodological trends, predominant network architectures, and reported clinical applications. A strong predominance of U-Net–based models and their variants was observed (9/16), alongside an increasing use of three-dimensional approaches (11/16) and a focus on vascular segmentation (6/16) or combined liver-and-vessel segmentation (6/16). Methodological strategies were equally divided between combined approaches (8/16) and single-step methods (8/16). Despite the high technical performance reported by several authors, the studies showed substantial heterogeneity in acquisition protocols, evaluation metrics, and dataset size, which hampers direct comparison and limits the translation of these results into clinical practice. It can be concluded that, although deep learning represents a significant advance in hepatic segmentation, methodological standardization and external model validation are still required for its consolidation in the clinical setting.
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