用解剖图谱生成3D肺部CT，可控且高效
LAND: Lung and Nodule Diffusion for 3D Chest CT Synthesis with Anatomical Guidance
- 基于潜空间扩散模型，用解剖掩膜控制生成
- 256×256×256体素，1毫米分辨率，单卡完成
- 需同时含肺与结节掩膜，否则结构失真
本文提出一种新的潜扩散模型，用于根据3D解剖掩膜生成高质量3D胸腔CT扫描。该方法在1毫米各向同性分辨率下，合成256×256×256大小的体数据，仅需单块中端显卡，显著降低计算成本。条件掩膜标记肺和结节区域，实现对输出解剖特征的精确控制。实验表明，仅以结节掩膜为条件会导致解剖错误结果，凸显全局肺结构信息对准确条件生成的重要性。该方法可生成含不同属性结节或无结节的多样化CT体积，为人工智能模型训练或医疗从业者提供有价值工具。
原文摘要 · Abstract (English)
This work introduces a new latent diffusion model to generate high-quality 3D chest CT scans conditioned on 3D anatomical masks. The method synthesizes volumetric images of size 256x256x256 at 1 mm isotropic resolution using a single mid-range GPU, significantly lowering the computational cost compared to existing approaches. The conditioning masks delineate lung and nodule regions, enabling precise control over the output anatomical features. Experimental results demonstrate that conditioning solely on nodule masks leads to anatomically incorrect outputs, highlighting the importance of incorporating global lung structure for accurate conditional synthesis. The proposed approach supports the generation of diverse CT volumes with and without lung nodules of varying attributes, providing a valuable tool for training AI models or healthcare professionals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。