Reconstruction First: What Drives Reliable Coronary Vessel Recovery?

Coronary vessel segmentation is structurally fragile: a few missed pixels can disconnect thin distal branches even when overall regional overlap is high. We ask whether a substantial part of this fragility can be addressed by better preserving and reconstructing fine local vessel evidence, and what additional benefit structure-aware modeling provides once reconstruction is strengthened. To disentangle these contributions, we develop and systematically decompose a reconstruction-first framework centered on a Context-Guided Vessel Reconstruction Decoder (CVRD), which preserves high-resolution local evidence, integrates multi-level contextual features to guide its interpretation, and progressively restores full-resolution vessel predictions. In controlled component ablations, weakening local detail extraction or replacing staged recovery produces the largest drops in both IoU and clDice among the tested reconstruction components. Across XACV, DCA1, and XCAD, the streamlined model yields consistent results over multiple seeds. Structural auxiliary objectives provide small but repeatable gains, but matched non-structural supervision and inference-time controls do not support a geometry-specific explanation for these improvements. Together, these results support reconstruction quality as a key contributor to coronary vessel recovery, while the evaluated structural supervision provides a smaller residual benefit once reconstruction is strong.