An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation

2025-01-14

AI Summary

To address the challenges of ambiguous boundaries and scarce annotations in coronary artery atherosclerotic plaque segmentation from coronary computed tomography angiography (CTA) images, this paper proposes a semi-supervised framework that jointly enforces intra-frame and inter-frame topological consistency. Methodologically, we introduce skeleton-aware distance transform (SDT) to guide intra-frame topological constraints and design a skeleton-boundary flow model to capture structural continuity across adjacent frames—without requiring additional annotations—thereby preserving vascular topology integrity. The framework integrates a dual-task segmentation network, unsupervised pixel-wise flow estimation, consistency regularization, and Curved Planar Reformation (CPR) preprocessing. Evaluated on two CTA datasets, our method achieves performance comparable to fully supervised state-of-the-art methods and significantly outperforms existing semi-supervised approaches. Furthermore, strong generalization is demonstrated on the ACDC dataset.

Technology Category

• Computer Vision: Segmentation
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Semi-Supervised Learning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Enhancing the precision of segmenting coronary atherosclerotic plaques from CT Angiography (CTA) images is pivotal for advanced Coronary Atherosclerosis Analysis (CAA), which distinctively relies on the analysis of vessel cross-section images reconstructed via Curved Planar Reformation. This task presents significant challenges due to the indistinct boundaries and structures of plaques and blood vessels, leading to the inadequate performance of current deep learning models, compounded by the inherent difficulty in annotating such complex data. To address these issues, we propose a novel dual-consistency semi-supervised framework that integrates Intra-frame Topological Consistency (ITC) and Cross-frame Topological Consistency (CTC) to leverage labeled and unlabeled data. ITC employs a dual-task network for simultaneous segmentation mask and Skeleton-aware Distance Transform (SDT) prediction, achieving similar prediction of topology structure through consistency constraint without additional annotations. Meanwhile, CTC utilizes an unsupervised estimator for analyzing pixel flow between skeletons and boundaries of adjacent frames, ensuring spatial continuity. Experiments on two CTA datasets show that our method surpasses existing semi-supervised methods and approaches the performance of supervised methods on CAA. In addition, our method also performs better than other methods on the ACDC dataset, demonstrating its generalization.

Problem

• CT Angiography
• Plaque Segmentation
• Cardiovascular Disease

Innovation

• Dual Consistency Semi-supervised Framework
• Vascular Plaque Recognition
• CT Angiography (CTA)