Anatomically Guided Cascaded U-Net Ensemble for Coronary Artery Calcification Segmentation in Cardiac CT.

Accurate segmentation of coronary artery calcifications (CAC) from cardiac CT is challenged by class imbalance, small lesion size, and anatomical ambiguity. We present an anatomically guided, cascaded framework that couples heart and vessel priors with a heterogeneous U-Net ensemble for robust, vessel-aware CAC segmentation. First, a ResU-Net trained on MM-WHS isolates the heart region of interest (ROI). Second, a ResU-Net trained on ASOCA-using Frangi vesselness enhancement-segments the coronary arteries, yielding vessel masks that constrain downstream lesion detection. Third, calcifications are segmented within the vessel-constrained ROI using an ensemble of U-Net variants (baseline U-Net, Residual U-Net, Attention U-Net, UNet++). At inference, a rank-based selective fusion strategy prioritizes predictions with strong morphological consistency and vessel conformity, suppressing false positives. On the Stanford COCA gated dataset, the proposed ensemble outperforms individual models (Dice 84.25%, sensitivity 87.10%, specificity 98.00%), with ablations demonstrating additional gains when vessel priors are integrated into selective fusion (Dice 85.50%, sensitivity 88.53%). Results confirm that combining dataset-specific anatomical priors with selective ensembling improves boundary sharpness, small-lesion detectability, and anatomical plausibility, supporting reliable CAC segmentation in clinical imaging workflows.