AnaDiffusion: Anatomically Compositional Latent Diffusion for Controllable 3D Brain MRI Generation
3D brain MRI generation has made significant advances in medical imaging, simulation, and controllable anatomical analysis. However, existing generative models typically synthesize 3D volumes monolithically, often overlooking regional anatomical structures and limiting local controllability. To address these limitations, we introduce AnaDiffusion, an anatomically compositional latent diffusion framework that factorizes the generation process into distinct, anatomically meaningful regions, followed by part-to-whole assembly and global refinement. Our approach first trains part diffusion models to capture local structural priors. We then inject an assembled anatomical composite of the parts...
| Authors | Huiwen Han, Lulin Liu, Bangya Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 12 October 2026 |
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