Accurate liver segmentation from three-dimensional (3D) computed tomography (CT) volumes is a critical step in computer-aided diagnosis, surgical planning, and disease quantification. Despite substantial progress in deep learning, achieving robust and generalizable liver segmentation remains challenging due to complex organ boundaries, pathological variations, and domain shifts across scanners. This review provides a comprehensive overview of 3D volumetric liver segmentation techniques, spanning from classical model-based methods to contemporary transformer-driven frameworks. We categorize existing methods into three paradigms: (1) classical statistical and atlas-based methods, (2) deep conv... More >