Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score accuracy in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 accuracy of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of our proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. We provide the code and model weights for Med-DDPM on our GitHub repository (https://github.com/mobaidoctor/med-ddpm/) to support reproducibility.

翻译：医疗领域的人工智能，尤其是医学影像分析，常受数据稀缺与隐私问题困扰。为此，我们提出Med-DDPM——一种专为三维语义脑MRI合成设计的扩散模型。该模型通过整合语义条件生成，有效缓解数据与隐私难题：将条件图像按通道维度拼接至模型输入，实现生成过程的精准控制。与现有三维脑影像合成方法相比，Med-DDPM展现出卓越的稳定性与性能，可生成多样化、解剖结构一致且视觉保真度高的图像。在肿瘤分割任务的Dice系数评估中，Med-DDPM达0.6207，逼近真实图像的0.6531，并显著优于基线模型。将该模型与真实图像联合使用时，分割精度进一步提升至0.6675，充分彰显本方法在数据增强领域的潜力。作为首个将扩散模型应用于三维语义脑MRI合成的研究，Med-DDPM不仅能生成高质量图像，其语义条件特性更在生物医学图像匿名化方面展现重要价值，有效应对数据与隐私挑战。为支持研究可复现性，我们已在GitHub仓库（https://github.com/mobaidoctor/med-ddpm/）公开Med-DDPM代码与模型权重。