用可控体积调节生成腹部解剖结构，支持多器官独立建模。
AbdomenGen: Sequential Volume-Conditioned Diffusion Framework for Abdominal Anatomy Generation
- 通过体积控制标量分离器官大小与体型，实现可解释的体积调节。
- 肝脏分割重合度达0.83±0.05，单器官调节范围覆盖[-3,+3] VCS。
- 适合医学模拟、肝肿大等临床研究中的可控解剖生成。
计算人体模型广泛用于医学成像研究，但现有系统在生成可控且具临床意义的解剖变异方面仍受限。本文提出AbdomenGen，一种序列化体素条件扩散框架，用于可控腹部解剖生成。引入体积控制标量（VCS），通过标准化残差将器官大小与体型解耦，实现可解释的体积调控。器官掩码按顺序生成，基于躯干掩码和先前生成结构进行条件约束，保持整体解剖一致性的同时支持多器官独立控制。在11个腹部器官上，该框架实现高几何保真度（如肝脏Dice值为0.83±0.05），单器官调节在[-3,+3] VCS范围内稳定，且多器官调控具备解耦性。以MERLIN数据库中肝肿大队列为样本，基于Wasserstein距离的VCS选择使训练数据分布距离降低73.6%。结果表明该方法适用于校准、分布感知的腹部解剖生成，适合构建可控解剖体模与仿真研究。
原文摘要 · Abstract (English)
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 \textbf{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 $0.83 \pm 0.05$), 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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