GASNet: Progressive Resolution-Aware Supervision and Gabor Guidance for Accurate Liver Vessel Segmentation

High-precision segmentation of liver vessels is crucial for surgical planning and clinical diagnosis. Yet, it remains challenging due to intricate vascular structures and the low contrast inherent in CT images. We propose GASNet, an innovative liver vessel segmentation network that integrates progressive resolution-aware supervision with a learnable Gabor multi-filter module. GASNet generates hierarchical segmentation masks and applies adaptive supervision at intermediate layers to enhance multi-scale feature learning. Built upon a ConvNeXt backbone, the network integrates a Multi-Scale Feature Refiner and Dilated Convolutional modules to improve structural modeling capacity. We evaluate GASNet on two public datasets (LiVS and MSD) as well as aselfconstructed dataset, LVTSD. GASNet consistently outperforms state-of-the-art methods and demonstrates superior capability in distinguishing hepatic and portal veins on the revised MSD dataset, achieving a Dice score of 0.836 for hepatic veins and 0.820 for portal veins. The implementation is available at https://github.com/HappyBot516/GASNet.