DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image

2025-06-06

AI Summary

Fine vessel structures in 3D medical imaging are prone to fragmentation and discontinuity when extracted via pixel-wise segmentation. To address this, we propose a deformable centerline representation—a continuous, topology-aware geometric modeling paradigm. Our method introduces a novel node-edge graph-based centerline formulation that intrinsically ensures structural connectivity, noise robustness, and clinical interpretability. Integrated with graph neural networks, deformable convolutions, and differentiable curve parameterization, it forms an end-to-end generative and refinement framework. A cascaded training strategy is further designed to enforce geometric priors—including smoothness, continuity, and anatomical plausibility. Evaluated on four benchmark 3D vascular datasets, our approach achieves significant improvements over state-of-the-art methods in both centerline accuracy and completeness. Moreover, curved planar reformation (CPR) surface reconstruction demonstrates high anatomical fidelity and clinical utility, validating its practical applicability in interventional planning and diagnosis.

Technology Category

• Computer Vision: Segmentation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous methods have commonly relied on discrete representation like mask, often resulting in local fractures or scattered fragments due to the inherent limitations of the per-pixel classification paradigm. In this work, we introduce DeformCL, a new continuous representation based on Deformable Centerlines, where centerline points act as nodes connected by edges that capture spatial relationships. Compared with previous representations, DeformCL offers three key advantages: natural connectivity, noise robustness, and interaction facility. We present a comprehensive training pipeline structured in a cascaded manner to fully exploit these favorable properties of DeformCL. Extensive experiments on four 3D vessel segmentation datasets demonstrate the effectiveness and superiority of our method. Furthermore, the visualization of curved planar reformation images validates the clinical significance of the proposed framework. We release the code in https://github.com/barry664/DeformCL

Problem

• Accurately extract 3D blood vessels from medical images
• Overcome local fractures in discrete vessel representations
• Improve connectivity and robustness in vessel segmentation

Innovation

• Deformable centerlines for continuous vessel representation
• Cascaded training pipeline for enhanced properties
• Noise-robust connectivity in 3D medical imaging