用自回归模型生成逼真血管结构，首次实现血管几何的序列化合成。
VesselGPT: Autoregressive Modeling of Vascular Geometry
- 先用VQ-VAE将血管编码为离散词汇，再用GPT-2逐段生成
- 重建精度高，离散表示紧凑，保留关键形态细节
- 适合医学图像生成、个性化治疗规划等场景
解剖树对临床诊断和治疗规划至关重要，但其复杂多样的几何结构给精准建模带来挑战。受大语言模型启发，我们提出一种自回归方法来合成解剖树。该方法首先利用VQ-VAE架构将血管结构嵌入学习得到的离散词汇表，再通过GPT-2模型进行自回归生成。该方法能有效捕捉复杂的几何形态与分支模式，实现逼真的血管树合成。全面的定性与定量评估表明，该技术在保持紧凑离散表示的同时，实现了高保真度的树结构重建。此外，采用B样条表示血管截面，保留了以往方法常忽略的关键形态细节。据我们所知，这是首个以自回归方式生成血液血管的工作。代码已公开于https://github.com/LIA-DiTella/VesselGPT-MICCAI。
原文摘要 · Abstract (English)
Anatomical trees are critical for clinical diagnosis and treatment planning, yet their complex and diverse geometry make accurate representation a significant challenge. Motivated by the latest advances in large language models, we introduce an autoregressive method for synthesizing anatomical trees. Our approach first embeds vessel structures into a learned discrete vocabulary using a VQ-VAE architecture, then models their generation autoregressively with a GPT-2 model. This method effectively captures intricate geometries and branching patterns, enabling realistic vascular tree synthesis. Comprehensive qualitative and quantitative evaluations reveal that our technique achieves high-fidelity tree reconstruction with compact discrete representations. Moreover, our B-spline representation of vessel cross-sections preserves critical morphological details that are often overlooked in previous' methods parameterizations. To the best of our knowledge, this work is the first to generate blood vessels in an autoregressive manner. Code is available at https://github.com/LIA-DiTella/VesselGPT-MICCAI.
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