Coronary Artery Segmentation Framework

• Coronary artery segmentation is a computational framework that accurately extracts vessel structures using deep learning and image processing techniques.
• It employs advanced preprocessing, multi-scale feature analysis, and domain adaptation to overcome low contrast and imaging artifacts.
• Robust post-processing enhances vessel continuity and anatomical accuracy, supporting applications like stenosis grading and CAD risk stratification.

A coronary artery segmentation framework refers to an integrated computational system designed to accurately extract the spatial extent and morphology of coronary arteries from medical imaging data, typically X-ray angiograms, coronary computed tomography angiography (CCTA), or invasive coronary angiography (ICA). Modern frameworks combine algorithmic innovation—involving deep learning architectures, anatomical priors, multi-scale feature processing, and advanced domain adaptation—with imaging-specific pipelines to address the complex topology, scale variation, and imaging artifacts intrinsic to coronary vasculature analysis.

1. Architectural Principles and Core Components

The foundational architectural paradigms for coronary artery segmentation incorporate both classical and deep learning elements. Common technical motifs include:

• Encoder–decoder backbones: U-Net and its variants (often with residual, dense, or attention-augmented encoders) prevail for pixel-wise segmentation, leveraging skip connections to retain low-level spatial information while building multi-scale semantic hierarchies (Silva et al., 2021, Huang et al., 14 Dec 2025, Yao et al., 27 Apr 2025).
• Multi-branch or ensemble designs: Several frameworks use ensemble models or parallel branches to harness different input pre-processings, capture both global and local image features, or handle anatomically distinct vessel classes (e.g., LCA vs. RCA) (Liu et al., 2023, Ku et al., 2023, Dong et al., 17 Jul 2025).
• Graph and mesh-based pipelines: In certain approaches, the segmentation output is a directly parameterized surface mesh, optimized with shape priors and geometric regularizers via graph convolutional networks or cascaded Unet-GCN architectures (Yang et al., 2023, Wolterink et al., 2019).
• Domain adaptation modules: When transferring knowledge between related domains (e.g., retinal vessels to coronary arteries), frameworks may introduce customized normalization (e.g., vesselness-specific batch normalization) and self-ensembling consistency constraints (Zhang et al., 2021).

2. Signal Preprocessing and Vessel Enhancement

Robust preprocessing is critical in coronary artery segmentation pipelines due to low vessel–background contrast, imaging artifacts, and variable lighting or noise. Preprocessing modules may include:

Enhancement Method	Brief Description	Framework Example
CLAHE	Improves vessel contrast via local histogram equalization.	(Hassan et al., 31 Oct 2025, Liu et al., 2023)
Multiscale top-hat	Emphasizes tubular structures across scales using morphological filtering.	(Liu et al., 2023, Fazlali et al., 2017)
Hessian/vesselness filters	Extracts vessel-like features via multi-scale analysis of second derivatives.	(Mu et al., 10 Sep 2025, Fazlali et al., 2017)
Ben Graham enhancement	Global mean correction and border artifact suppression.	(Hassan et al., 31 Oct 2025)
Directional filters	Highlights line-like vascular features using frequency or orientation filtering.	(Liu et al., 2023)

Preprocessing outputs may be concatenated as multi-channel inputs to CNN-based backbones, or supplied in parallel to ensemble detectors. This multichannel/ensemble strategy consistently yields measurable improvements in Dice and IoU metrics.

3. Learning Schemes: Supervised, Semi-supervised, and Weakly Supervised

Coronary artery segmentation frameworks span a spectrum of learning setups:

• Fully supervised: U-Net variations or transformer-based architectures trained end-to-end with dense manual labels (Silva et al., 2021, Liu et al., 2023, Huang et al., 14 Dec 2025). Encoder backbones are frequently frozen if pretrained on large datasets.
• Semi-supervised/Domain adaptation: SS-CADA leverages public labeled fundus imagery and minimal XA annotations, utilizing VSBN to address domain shift and a mean-teacher strategy for maximizing gains from unlabeled data (Zhang et al., 2021). Dual consistency constraints (intra- and cross-frame) can further enforce shape/topology in settings with scarce labels (Zhang et al., 14 Jan 2025).
• Weakly supervised/Partial annotation: When only partial vessel annotations are feasible, frameworks employ pseudo-label propagation, prototype learning, and progressively refined error correction (Zhang et al., 2023), often achieving Dice improvements of 11–16 points over standard weakly supervised baselines.

Network outputs are commonly regularized by Dice, cross-entropy, and specialized topology-aware losses (e.g., clDice, tree-connectivity terms, or feature-prototype consistency metrics).

4. Post-processing, Refinement, and Tree Topology Correction

Post-processing steps address over-segmentation, vessel disconnections, anatomical plausibility, and misclassification:

• Connected component and contour filtering: Small spurious islands are discarded based on area thresholds (Hassan et al., 31 Oct 2025).
• Skeletonization and patch-line reconnection: Terminal points of skeletons are joined to restore broken vessel continuity, with acceptance based on supportive vessel pixel density (Hassan et al., 31 Oct 2025).
• Graph-based tree assembly and logic sorting: Detected vessel segments are mapped to anatomical graphs (e.g., SYNTAX segments), enforcing parent–child constraints and circulation-specific topology (Liu et al., 2023). In multi-class schemes, a dedicated refinement model corrects misclassified side branches (e.g., MPSeg's EfficientNet/ResNet refiners for LCA) (Ku et al., 2023).
• Feature-prototype weighting: Continuity and structural similarity are boosted by prototype-based attention in global post-processing (Zhang et al., 2023).

These steps enhance the reliability of the coronary tree segmentation, particularly in challenging imaging conditions.

5. Quantitative Evaluation, Benchmarks, and Clinical Considerations

Metrics for performance assessment include Dice coefficient, Intersection over Union (IoU), centerline Dice (clDice), sensitivity, precision, average symmetric surface distance (ASSD), and Hausdorff distance (HD95):

Model/Framework	Image Modality	Key Dice (%)	clDice	Additional Highlights	Reference
EfficientUNet++	2D XA	89.0	—	Artery class only. Catheter DSC: 75.3. Generalized Dice score: 0.9234	(Silva et al., 2021)
SFD-Mamba2Net	ICA (2D)	88.1	—	Outperforms 7 baselines on 8 metrics. Stenosis TPR: 0.60, PPV: 0.64	(Mu et al., 10 Sep 2025)
Parallel ViT-CNN+CVF+EUR	3D CCTA	90.1	—	Generalizes across three datasets, surpasses 9 SOTA methods, cross-domain generalization strong	(Dong et al., 17 Jul 2025)
CASR-Net (DenseNet121+Self-ONN)	X-ray	76.1	0.79	Particularly strong for narrow, stenotic vessel continuity; validated 5-fold CV	(Hassan et al., 31 Oct 2025)
Anatomy-guided frq. U-Net	3D CCTA	80.8	—	HD95: 9.8mm; Ablation confirms joint frequency-spatial and anatomical priors are synergistic	(Huang et al., 14 Dec 2025)
MGFA-Net (dual encoder)	3D CCTA	85.0	—	HD95: 6.1mm; Stenosis TPR: +5.46% versus 3D U-Net	(Yao et al., 27 Apr 2025)
Knowledge distillation (LightVessel)	X-ray	77.9	—	Matches large teacher models with 1/6th params, 1% FLOPs	(Dang et al., 2022)
Geometry-based cascade (Unet+GCN)	CCTA	77.8/89.5*	—	Outputs watertight surfaces, NoS=2, ablates branch/fork artifacts seen in voxel methods	(Yang et al., 2023)
Weakly supervised (PVA+proto)	CCTA	71.5*	—	24% annotation budget, trunk continuity matches fully supervised	(Zhang et al., 2023)
SS-CADA (domain adap.)	X-ray	78.8	—	Only 20 annotated images, leverages public retinal fundus data	(Zhang et al., 2021)

A indicates CCA-200 (own)/*ASOCA (public) performance.

Clinical significance centers on the frameworks’ ability to preserve main trunk and branch continuity, support expert-level segmentation (e.g., Dice within interrater variability), and enable downstream tasks including stenosis grading, quantitative flow analysis, and CAD risk stratification.

6. Challenges, Limitations, and Emerging Directions

Difficulties persist in achieving topological correctness, robustness across patient/center variability, and high recall for small/distal branches:

• Domain adaptation remains essential in low-annotation scenarios and for cross-center generalizability, but handling domain shifts due to imaging protocol and anatomy is challenging.
• Topology preservation is often limited by standard losses; integrating clDice or skeleton/boundary consistency, as well as graph-based losses, shows measurable improvements (Zhang et al., 14 Jan 2025, Zhang et al., 2023).
• Computational expense is non-trivial for 3D and mesh-based methods, motivating investigation into model compression, real-time 3D operators, and efficient ensemble strategies (Huang et al., 14 Dec 2025, Hassan et al., 31 Oct 2025).
• Weak and partial supervision are increasingly feasible with prototype learning, pseudo-label propagation, and domain adaptation, reducing annotation burden by ~4× with modest loss in trunk-branch accuracy (Zhang et al., 2023, Zhang et al., 2021).

Continued research targets real-time, uncertainty-aware, and annotation-efficient pipelines, with emphasis on explicit anatomical priors, temporal (video) continuity, multimodal fusion, and integration of diagnostic downstream tasks (stenosis assessment, functional analysis) into unified clinical pipelines.