CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models

ArXi:2509.08015v3 Announce Type: replace-cross Generative models of 3D cardiovascular anatomy can synthesize informative structures for clinical research and medical device evaluation, but face a trade-off between geometric controllability and realism. We propose CardioComposer: a programmable, inference-time framework for generating multi-class anatomical label maps from interpretable ellipsoidal primitives. These primitives represent geometric attributes such as the size, shape, and position of discrete substructures.