用扩散模型生成高精度3DCT影像，解决医疗数据少、标注贵难题
MAISI: Medical AI for Synthetic Imaging
- 基于扩散模型与压缩网络生成3D CT影像
- 支持127个解剖结构的精准标注生成
- 适合医学图像数据增强与隐私保护场景
医学影像分析面临数据稀缺、标注成本高和隐私问题。本文提出医学人工智能合成成像（MAISI），利用扩散模型生成合成3D CT图像以应对这些挑战。MAISI结合基础体素压缩网络与潜在扩散模型，可生成最高达512×512×768体素分辨率的高分辨率CT图像，支持灵活的体素维度与间距。通过引入ControlNet，MAISI能处理包含127个解剖结构的器官分割作为附加条件，生成具有精确标注的合成图像，适用于多种下游任务。实验结果表明，MAISI生成的图像在多样区域与条件下均具高度真实感与解剖准确性，展现出利用合成数据缓解上述挑战的巨大潜力。
原文摘要 · Abstract (English)
Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.
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