ACE-ProtoNet: Adaptive covariance eigen-gate and uncertainty-aware prototype learning for coronary artery segmentation.
Authors
Affiliations (8)
Affiliations (8)
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China; Institute of Medical Artificial Intelligence, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China. Electronic address: [email protected].
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China; Institute of Medical Artificial Intelligence, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China. Electronic address: [email protected].
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China. Electronic address: [email protected].
- College of Computer Science, Sichuan University, Chengdu, 610065, China. Electronic address: [email protected].
- Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China. Electronic address: [email protected].
- Institute of Medical Artificial Intelligence, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China. Electronic address: [email protected].
- School of AI, Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China. Electronic address: [email protected].
- National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710004, China; School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049, China. Electronic address: [email protected].
Abstract
Accurate segmentation of coronary arteries from Coronary CT Angiography (CCTA) is essential for quantitative stenosis evaluation, plaque characterization, and surgical planning. However, low vessel-to-background contrast, high anatomical variability, and the complex, tree-like vascular morphology pose significant challenges to automated segmentation. To address these challenges, we propose ACE-ProtoNet, a unified framework that couples an Adaptive Covariance Eigen-Gate (ACE-Gate) with an Uncertainty-aware Prototype Learning Head (UPL-Head) to achieve robust and accurate coronary artery segmentation. The architecture is built upon a parallel dual-encoder backbone which synergizes a partially frozen Vision Foundation Model (VFM) for global structural encoding with a trainable CNN for fine-grained local feature extraction. To reconcile the heterogeneity between these feature streams, the ACE-Gate explicitly models inter-channel dependencies through covariance analysis and eigenvalue decomposition, yielding statistically grounded, channel-wise gating for principled feature integration. Meanwhile, the UPL-Head leverages voxel-wise predictive uncertainty to modulate prototype-guided attention and dynamically update prototypes during training, thereby enhancing representation robustness in hard-to-classify regions and improving overall segmentation accuracy. Extensive experiments on two in-house and four public datasets demonstrate that ACE-ProtoNet consistently outperforms twelve state-of-the-art methods across multiple metrics, exhibiting superior cross-domain generalization as well as strong cross-modality and cross-anatomy transferability. The code is available at .