用生成模型合成脑部分割图，保留解剖结构和病灶特征。
Anatomy-Preserving Latent Diffusion for Generation of Brain Segmentation Masks with Ischemic Infarct
- 先学解剖结构的潜在表示，再在潜空间生成新图像。
- 生成结果保持脑部整体结构与组织语义，无像素级伪影。
- 适合数据稀缺的医学图像生成，尤其适用于缺血性梗死研究。
高质量分割掩码的缺乏是医学图像分析的一大瓶颈，尤其是在非增强CT（NCCT）神经影像中，手动标注成本高且差异大。为此，我们提出一种解剖结构保持的生成框架，用于无条件合成多类脑部分割掩码，包括缺血性梗死。该方法结合仅在分割掩码上训练的变分自编码器（VAE），学习解剖潜空间表示，并利用扩散模型在该潜空间中从纯噪声生成新样本。推理时，通过冻结的VAE解码器对去噪潜向量进行解码，获得合成掩码，可选地通过二值提示粗略控制病灶是否存在。定性结果显示，生成掩码保持了全局脑解剖结构、离散组织语义及真实变异，避免了像素空间生成模型常见的结构伪影。总体而言，该框架为数据稀缺的医学影像场景提供了简单且可扩展的解剖感知掩码生成方案。
原文摘要 · Abstract (English)
The scarcity of high-quality segmentation masks remains a major bottleneck for medical image analysis, particularly in non-contrast CT (NCCT) neuroimaging, where manual annotation is costly and variable. To address this limitation, we propose an anatomy-preserving generative framework for the unconditional synthesis of multi-class brain segmentation masks, including ischemic infarcts. The proposed approach combines a variational autoencoder trained exclusively on segmentation masks to learn an anatomical latent representation, with a diffusion model operating in this latent space to generate new samples from pure noise. At inference, synthetic masks are obtained by decoding denoised latent vectors through the frozen VAE decoder, with optional coarse control over lesion presence via a binary prompt. Qualitative results show that the generated masks preserve global brain anatomy, discrete tissue semantics, and realistic variability, while avoiding the structural artifacts commonly observed in pixel-space generative models. Overall, the proposed framework offers a simple and scalable solution for anatomy-aware mask generation in data-scarce medical imaging scenarios.
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