Laparoscopic image segmentation of the hepatobiliary system: Current status and advances from a deep learning perspective.

Hepatobiliary laparoscopy provides a minimally invasive approach for treating hepatobiliary diseases, complex anatomical variations, and temporary intraoperative tissue occlusion, rendering these procedures highly challenging. To evaluate how computer vision can overcome these visual limitations, this review systematically analyses deep learning-based laparoscopic image segmentation for the hepatobiliary system. Based on searches across Google Scholar, Web of Science, and PubMed, 16 eligible studies focusing on pixel-level laparoscopic hepatobiliary image segmentation published between 2018 and early 2026 were selected and synthesised. Findings reveal that current research predominantly focuses on gallbladder segmentation, followed by the liver and bile ducts. Methodologically, reviewed network architectures-ranging from foundational convolutional networks to hybrid vision models-are categorised into seven goal-driven optimisation schemes, primarily addressing spatiotemporal modelling, class imbalance mitigation, and model lightweighting. Quantitative synthesis highlights a critical trade-off between algorithmic performance and engineering overhead; notably, while conventional three-dimensional (3D) models enhance spatiotemporal continuity, they demand substantial computational and memory resources. Furthermore, we delineate key bottlenecks hindering clinical translation, including multicentre data variance, alarm fatigue, and compliance with DECIDE-AI reporting guidelines. Ultimately, by mapping this translational landscape, this review provides a roadmap to accelerate the deployment of deep learning paradigms in laparoscopic hepatobiliary surgery.