3D medical images frequently contain complex irregular structures such as blood vessels and tumors, characterized by ill-defined borders and intricate topologies. Accurately segmenting these structures presents considerable challenges. Traditional 3D medical image semantic segmentation methods mainly focus on learning local features for pixel-level classification but often struggle to accurately capture complex global topological relationships. Some specialized methods attempt to address this by incorporating topological information as an additional training loss to improve pixel-level predictions. However, these approaches have not consistently demonstrated effectiveness in handling complex topological structures, particularly in medical imaging. In this paper, we propose a novel architectural component, the Topological Enhancement Learning Module (TELM), a plug-and-play unit that can be seamlessly integrated into existing 3D segmentation networks. TELM improves segmentation performance on complex topologies by explicitly encoding and leveraging global spatial topological information to guide the training process. Extensive experiments across multiple 3D datasets demonstrate TELM’s ability to significantly improve the segmentation accuracy of baseline models, validating its effectiveness in enhancing model robustness for complex and irregular structures in 3D medical imaging.