用隐式神经表示实现可调控的解剖结构生成，支持精细形状控制。
Steerable Anatomical Shape Synthesis with Implicit Neural Representations
- 基于隐式神经表示构建可调节生成模型，支持拓扑变化。
- 在甲状腺等结构上实现高保真形状重建与合理形态生成。
- 适合需定制化解剖数据的医学模拟与虚拟临床试验。
解剖结构的生成建模在虚拟成像试验中至关重要，可避免体内实验和物理模型的高昂成本与限制。为提升临床相关性，生成模型需支持针对特定患者群体的精准控制，而非仅依赖随机采样。本文提出一种基于隐式神经表示的可调控生成模型。隐式神经表示天然支持拓扑变化，适用于甲状腺等形态多变的解剖结构。模型学习解耦的潜在表示，实现对形状变化的细粒度控制。评估涵盖重建精度与解剖合理性。结果表明，该模型在保持高质量形状生成的同时，能够实现目标导向的解剖修改。
原文摘要 · Abstract (English)
Generative modeling of anatomical structures plays a crucial role in virtual imaging trials, which allow researchers to perform studies without the costs and constraints inherent to in vivo and phantom studies. For clinical relevance, generative models should allow targeted control to simulate specific patient populations rather than relying on purely random sampling. In this work, we propose a steerable generative model based on implicit neural representations. Implicit neural representations naturally support topology changes, making them well-suited for anatomical structures with varying topology, such as the thyroid. Our model learns a disentangled latent representation, enabling fine-grained control over shape variations. Evaluation includes reconstruction accuracy and anatomical plausibility. Our results demonstrate that the proposed model achieves high-quality shape generation while enabling targeted anatomical modifications.
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