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

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.

Segmentation of Soft atherosclerotic plaques using active contour models

Detection of non-calcified plaques in the coronary tree is a challenging problem due to the nature of comprising substances. Hard plaques are easily discernible in CTA data cloud due to apparent bright behaviour, therefore many approaches have been proposed for automatic segmentation of calcified plaques. In contrast soft plaques show very small difference in intensity with respect to surrounding heart tissues & blood voxels. This similarity in intensity makes the isolation and detection of soft plaques very difficult. This work aims to develop framework for segmentation of vulnerable plaques with minimal user dependency. In first step automatic seed point has been established based on the fact that coronary artery behaves as tubular structure through axial slices. In the following step the behaviour of contrast agent has been modelled mathematically to reflect the dye diffusion in respective CTA volume. Consequently based on detected seed point & intensity behaviour, localized active contour segmentation has been applied to extract complete coronary tree. Bidirectional segmentation has been applied to avoid loss of coronary information due to the seed point location whereas auto adjustment feature of contour grabs new emerging branches. Medial axis for extracted coronary tree is generated using fast marching method for obtaining curve planar reformation for validation of contrast agent behaviour. Obtained coronary tree is to be evaluated for soft plaques in second phase of this research.