🤖 AI Summary
Existing 3D anatomical generative models struggle to simultaneously achieve geometric controllability and anatomical fidelity. To address this, we propose an interpretable, programmable guidance framework based on ellipsoidal primitives. Our method decouples the modeling of size, shape, and position parameters for individual anatomical structures, integrates multi-tissue segmentation maps and geometric moment loss into an unconditional diffusion model, and injects 3D ellipsoidal guidance signals during the reverse diffusion process. This enables inference-time, independent or compositional geometric editing of multiple tissues—without retraining. Evaluated on complex multi-organ structures such as the heart, our approach achieves millimeter-level morphological editing precision while preserving high anatomical fidelity. It supports flexible, structured generation of anatomically plausible configurations, significantly enhancing controllability and interpretability in medical image synthesis.
📝 Abstract
Generative models of 3D anatomy, when integrated with biophysical simulators, enable the study of structure-function relationships for clinical research and medical device design. However, current models face a trade-off between controllability and anatomical realism. We propose a programmable and compositional framework for guiding unconditional diffusion models of human anatomy using interpretable ellipsoidal primitives embedded in 3D space. Our method involves the selection of certain tissues within multi-tissue segmentation maps, upon which we apply geometric moment losses to guide the reverse diffusion process. This framework supports the independent control over size, shape, and position, as well as the composition of multi-component constraints during inference.