This study has achieved the first three-dimensional reconstruction of the Laennec membrane hepatic pedicle based on CT imaging, breaking through the technical bottleneck of visualizing and identifying thin anatomical structures (thickness only 23.56~79.86 μm). By combining enhanced CT with 3D U-Net deep learning algorithms, it has achieved sub-millimeter-level precision segmentation (accuracy up to 96.8%), significantly improving the display ability of anatomical variations of the hepatic pedicle. The study systematically reveals four types of variations in the main branches of the hepatic pedicle and establishes a morphological database containing length, outer diameter, and angle, providing an objective basis for individualized precise liver resection. On this basis, the hepatic pedicle sheath separation and occlusion clamp has been developed, and clinical verification shows that it can significantly shorten the processing time and operation time of the hepatic pedicle, reduce the incidence of bile duct injury (0 vs 20%, P=0.031), and improve the safety and efficiency of surgery. This study has achieved the full chain clinical transformation from imaging anatomy to instrument innovation, promoting the development of standardized liver resection technology with the Laennec membrane as the anatomical landmark, and has important clinical promotion value.