VesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT Images

2026-03-09

AI Summary

This work addresses the challenges of high annotation cost and structural complexity in extracting vessel centerlines from 3D CT images by introducing diffusion models to this task for the first time. The authors propose a coarse-to-fine, multi-scale centerline representation that captures vascular structures at varying levels of detail. To enhance the naturalness and robustness of the reconstructed centerlines, a voting-based aggregation strategy is integrated into the framework. Evaluated on public CT datasets, the proposed method significantly outperforms conventional approaches, achieving higher accuracy and producing more anatomically coherent vessel centerlines.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Structured Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Vessel centerline extraction from 3D CT images is an important task because it reduces annotation effort to build a model that estimates a vessel structure. It is challenging to estimate natural vessel structures since conventional approaches are deterministic models, which cannot capture a complex human structure. In this study, we propose VesselFusion, which is a diffusion model to extract the vessel centerline from 3D CT image. The proposed method uses a coarse-to-fine representation of the centerline and a voting-based aggregation for a natural and stable extraction. VesselFusion was evaluated on a publicly available CT image dataset and achieved higher extraction accuracy and a more natural result than conventional approaches.

Problem

• vessel centerline extraction
• 3D CT images
• diffusion models
• vascular structure
• medical image analysis

Innovation

• diffusion model
• vessel centerline extraction
• 3D CT image
• coarse-to-fine representation
• voting-based aggregation