A unified, threshold-free differentiable convex-shape prior based on quasi-concavity of the segmentation mask function. Zero-, first-, and second-order characterizations yield a midpoint convexification algorithm and compact convolutional losses that integrate seamlessly with modern segmentation networks via the proposed Convex Gradient Projection Module (CGPM). Accepted to CVPR 2026 as a Highlight.
