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：医疗保健领域的人工智能，尤其在医学影像中，面临数据稀缺和隐私问题的挑战。针对这些问题，我们提出了Med-DDPM——一种专为三维语义脑部MRI合成设计的扩散模型。该模型通过整合语义条件控制，有效解决了数据稀缺和隐私问题。具体方法是将条件图像沿通道维度拼接至模型输入，从而实现对图像生成的控制。与现有的三维脑部影像合成方法相比，Med-DDPM展现出更优越的稳定性和性能。它能生成多样化、解剖结构连贯且视觉保真度高的图像。在肿瘤分割任务的Dice评分精度上，Med-DDPM达到0.6207，接近真实图像的0.6531，并优于基线模型。与真实图像结合使用时，分割精度进一步提高至0.6675，彰显了我们提出的方法在数据增强方面的潜力。该模型是首个将扩散模型应用于三维语义脑部MRI合成并生成高质量图像的模型。其语义条件控制特性还显示出在生物医学影像匿名化方面的潜力，有助于解决数据和隐私问题。我们在GitHub仓库（https://github.com/mobaidoctor/med-ddpm/）中提供了Med-DDPM的代码和模型权重，以支持研究可复现性。