HENet: Hierarchical Enhancement Network for Pulmonary Vessel Segmentation in Non-contrast CT Images

Pulmonary vessel segmentation in computerized tomography (CT) images is essential for pulmonary vascular disease and surgical navigation. However, the existing methods were generally designed for contrast-enhanced images, their performance is limited by the low contrast and the non-uniformity of Hounsfield Unit (HU) in non-contrast CT images, meanwhile, the varying size of the vessel structures are not well considered in current pulmonary vessel segmentation methods. To address this issue, we propose a hierarchical enhancement network (HENet) for better image- and feature-level vascular representation learning in the pulmonary vessel segmentation task. Specifically, we first design an Auto Contrast Enhancement (ACE) module to adjust the vessel contrast dynamically. Then, we propose a Cross-Scale Nonlocal Block (CSNB) to effectively fuse multi-scale features by utilizing both local and global semantic information. Experimental results show that our approach achieves better pulmonary vessel segmentation outcomes compared to other state-of-the-art methods, demonstrating the efficacy of the proposed ACE and CSNB module. Our code is available at https://github.com/CODESofWenqi/HENet.