The Centerline-Cross Entropy Loss for Vessel-Like Structure Segmentation: Better Topology Consistency Without Sacrificing Accuracy

Achieving accurate vessel segmentation in medical images is crucial for various clinical applications, but current methods often struggle to balance topological consistency (preserving vessel network structure) with segmentation accuracy (overlap with ground-truth). Although various strategies have been proposed to address this challenge, they typically necessitate significant modifications to network architecture, more annotations, or entail prohibitive computational costs, providing only partial topological improvements. The clDice loss was recently proposed as an elegant and efficient alternative to preserve topology in tubular structure segmentation. However, segmentation accuracy is penalized and it lacks robustness to noisy annotations, mirroring the limitations of the conventional Dice loss. This work introduces the centerline-Cross Entropy (clCE) loss function, a novel approach which capitalizes on the robustness of Cross-Entropy loss and the topological focus of centerline-Dice loss, promoting optimal vessel overlap while maintaining faithful network structure. Extensive evaluations on diverse publicly available datasets (2D/3D, retinal/coronary) demonstrate clCE’s effectiveness. Compared to existing losses, clCE achieves superior overlap with ground truth while simultaneously improving vascular connectivity. This paves the way for more accurate and clinically relevant vessel segmentation, particularly in complex 3D scenarios. We share an implementation of the clCE loss function in github.com/cesaracebes/centerline_CE.