Same Branches, Different Trees: A Bifurcation Connectedness Metric for Coronary Artery Segmentation and FFR-CT Decision Agreement

2026-07-30

AI Summary

This study addresses the critical issue that branch disconnections in coronary artery segmentation lead to erroneous FFR-CT–based treatment decisions, a flaw undetected by conventional metrics such as Dice coefficient due to their insensitivity to topological connectivity. To bridge this gap, the work introduces Bifurcation Connectedness Score (BCS)—the first dedicated metric for assessing bifurcation integrity—and its differentiable variant, soft-BCS, to explicitly evaluate and optimize the topological fidelity of segmentation outputs. Validation through centerline analysis, FFR-CT hemodynamic simulations, and deep learning training demonstrates that BCS effectively disentangles two distinct properties: “branch recovery” and “connection preservation.” Experimental results show that higher BCS significantly improves agreement between FFR-CT–derived clinical decisions based on predicted versus ground-truth geometries (OR = 2.16), with the most pronounced benefits observed in severe stenosis cases.

Abstract

Fractional flow reserve derived from CT angiography (FFR-CT) simulates flow through a patient-specific vessel model, so its accuracy depends on the connectedness of the segmented tree, not only on volumetric overlap: a segmentation can reach high Dice yet sever a bifurcation, dropping the downstream subtree and reversing the treatment decision. Topology-aware losses such as clDice and Skeleton Recall act on the global centreline and can miss localised breaks. We study the Bifurcation Connectedness Score (BCS), which scores connectedness at each ground-truth bifurcation, and soft-BCS, its differentiable training surrogate. BCS captures a property of segmentation quality the standard metrics miss: it responds strongly to breaks in connectedness while staying largely unchanged under connectedness-preserving narrowing. Higher BCS accompanies closer agreement between the FFR-CT decisions a solver makes on predicted versus ground-truth geometry, most clearly in severe disease (OR 2.16, CI [1.23, 4.18]). Both decisions come from the same solver, so this reflects geometric, not clinical, fidelity. In training, soft-BCS and Skeleton Recall recover the same branches but build different trees. Recovering branches and keeping them connected are separable properties, so we recommend reporting a measure of each.

Problem

• coronary artery segmentation
• bifurcation connectedness
• FFR-CT decision agreement
• topology-aware evaluation
• vascular tree connectivity

Innovation

• Bifurcation Connectedness Score
• FFR-CT
• coronary artery segmentation
• topology-aware metric
• soft-BCS