GLCP: Global-to-Local Connectivity Preservation for Tubular Structure Segmentation

2025-07-28

AI Summary

Medical image segmentation of tubular structures (e.g., vasculature) often suffers from structural discontinuities, and existing methods struggle to simultaneously preserve global topological integrity and local geometric fidelity. To address this, we propose a global-to-local connectivity-preserving framework. Our key contributions are: (1) the Interactive Multi-Head Segmentation (IMS) module—the first to jointly model semantic segmentation, skeleton graphs, and local discontinuity regions in an end-to-end manner; and (2) the lightweight Dual-Attention Refinement (DAR) module, which integrates skeleton-guided attention and discontinuity-aware attention to enforce hierarchical connectivity constraints. Evaluated on multiple 2D and 3D medical imaging benchmarks, our method consistently outperforms state-of-the-art approaches, achieving higher segmentation accuracy while significantly reducing structural breaks, improving topological consistency, and enhancing geometric fidelity.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Accurate segmentation of tubular structures, such as vascular networks, plays a critical role in various medical domains. A remaining significant challenge in this task is structural fragmentation, which can adversely impact downstream applications. Existing methods primarily focus on designing various loss functions to constrain global topological structures. However, they often overlook local discontinuity regions, leading to suboptimal segmentation results. To overcome this limitation, we propose a novel Global-to-Local Connectivity Preservation (GLCP) framework that can simultaneously perceive global and local structural characteristics of tubular networks. Specifically, we propose an Interactive Multi-head Segmentation (IMS) module to jointly learn global segmentation, skeleton maps, and local discontinuity maps, respectively. This enables our model to explicitly target local discontinuity regions while maintaining global topological integrity. In addition, we design a lightweight Dual-Attention-based Refinement (DAR) module to further improve segmentation quality by refining the resulting segmentation maps. Extensive experiments on both 2D and 3D datasets demonstrate that our GLCP achieves superior accuracy and continuity in tubular structure segmentation compared to several state-of-the-art approaches. The source codes will be available at https://github.com/FeixiangZhou/GLCP.

Problem

• Addressing structural fragmentation in tubular structure segmentation
• Overcoming local discontinuity in global topological methods
• Improving accuracy and continuity in vascular network segmentation

Innovation

• Global-to-Local Connectivity Preservation (GLCP) framework
• Interactive Multi-head Segmentation (IMS) module
• Dual-Attention-based Refinement (DAR) module