AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation
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Abstract (arXiv)
Computational phantoms are widely used in medical imaging research, yet current systems to generate controlled, clinically meaningful anatomical variations remain limited. We present AbdomenGen, a sequential volume-conditioned diffusion framework for controllable abdominal anatomy generation. We introduce the Volume Control Scalar (VCS), a standardized residual that decouples organ size from body habitus, enabling interpretable volume modulation. Organ masks are synthesized sequentially, conditioning on the body mask and previously generated structures to preserve global anatomical coherence while supporting independent, multi-organ control. Across 11 abdominal organs, the proposed framework achieves strong geometric fidelity (e.g., liver dice 0.83 ± 0.05), stable single-organ calibration over [-3,+3] VCS, and disentangled multi-organ modulation. To showcase clinical utility with a hepatomegaly cohort selected from MERLIN, Wasserstein-based VCS selection reduces distributional distance of training data by 73.6% . These results demonstrate calibrated, distribution-aware anatomical generation suitable for controllable abdominal phantom construction and simulation studies.
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References · 9
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
- 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
- 2021Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapescited by 426
- 2022Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functionscited by 133
- 2024MAISI: Medical AI for Synthetic Imagingcited by 86
- 2023Anatomy Completor: A Multi-class Completion Framework for 3D Anatomy Reconstructioncited by 4
- 2023Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generationcited by 1
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