A topology-preserving three-stage framework for fully-connected coronary artery extraction

2025-04-02

AI Summary

Coronary CT image segmentation faces challenges including distal vessel thinness, complex topology, and low contrast, leading to over- and under-segmentation and hindering fully connected coronary tree reconstruction. To address these issues, we propose the first topology-preserving three-stage framework: (1) a centerline-enhanced loss function to improve segmentation accuracy for fine vessels; (2) a robust random-walk algorithm regularized by distance, probability, and directional cosine metrics to reconnect fragmented centerlines; and (3) implicit neural representations (INRs) for geometry-driven, continuous reconstruction of missing vascular segments. Evaluated on ASOCA and PDSCA datasets, our method achieves Dice scores of 88.53% and 85.07%, and Hausdorff distances of 1.07 mm and 1.63 mm—substantially outperforming state-of-the-art approaches. The framework ensures structural integrity and topological connectivity of the reconstructed coronary tree.

Technology Category

• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Web data integration and cleaning

Abstract

Coronary artery extraction is a crucial prerequisite for computer-aided diagnosis of coronary artery disease. Accurately extracting the complete coronary tree remains challenging due to several factors, including presence of thin distal vessels, tortuous topological structures, and insufficient contrast. These issues often result in over-segmentation and under-segmentation in current segmentation methods. To address these challenges, we propose a topology-preserving three-stage framework for fully-connected coronary artery extraction. This framework includes vessel segmentation, centerline reconnection, and missing vessel reconstruction. First, we introduce a new centerline enhanced loss in the segmentation process. Second, for the broken vessel segments, we further propose a regularized walk algorithm to integrate distance, probabilities predicted by a centerline classifier, and directional cosine similarity, for reconnecting the centerlines. Third, we apply implicit neural representation and implicit modeling, to reconstruct the geometric model of the missing vessels. Experimental results show that our proposed framework outperforms existing methods, achieving Dice scores of 88.53% and 85.07%, with Hausdorff Distances (HD) of 1.07mm and 1.63mm on ASOCA and PDSCA datasets, respectively. Code will be available at https://github.com/YH-Qiu/CorSegRec.

Problem

• Extracting fully-connected coronary arteries accurately
• Addressing over-segmentation and under-segmentation issues
• Reconstructing missing vessels with topology preservation

Innovation

• Centerline enhanced loss for segmentation
• Regularized walk algorithm for reconnection
• Implicit neural representation for reconstruction