The demand for artificial intelligence (AI) in healthcare is rapidly increasing. However, significant challenges arise from data scarcity and privacy concerns, particularly in medical imaging. While existing generative models have achieved success in image synthesis and image-to-image translation tasks, there remains a gap in the generation of 3D semantic medical images. To address this gap, we introduce Med-DDPM, a diffusion model specifically designed for semantic 3D medical image synthesis, effectively tackling data scarcity and privacy issues. The novelty of Med-DDPM lies in its incorporation of semantic conditioning, enabling precise control during the image generation process. Our model outperforms Generative Adversarial Networks (GANs) in terms of stability and performance, generating diverse and anatomically coherent images with high visual fidelity. Comparative analysis against state-of-the-art augmentation techniques demonstrates that Med-DDPM produces comparable results, highlighting its potential as a data augmentation tool for enhancing model accuracy. In conclusion, Med-DDPM pioneers 3D semantic medical image synthesis by delivering high-quality and anatomically coherent images. Furthermore, the integration of semantic conditioning with Med-DDPM holds promise for image anonymization in the field of biomedical imaging, showcasing the capabilities of the model in addressing challenges related to data scarcity and privacy concerns.

翻译：人工智能在医疗健康领域的需求日益增长。然而，数据稀缺性和隐私问题带来了重大挑战，尤其是在医学影像领域。尽管现有生成模型在图像合成和图像到图像翻译任务中取得了成功，但在三维语义医学图像生成方面仍存在空白。为填补这一空白，我们提出了Med-DDPM——一种专为语义三维医学图像合成设计的扩散模型，有效应对了数据稀缺和隐私问题。Med-DDPM的创新之处在于其引入了语义条件机制，能够在图像生成过程中实现精确控制。我们的模型在稳定性和性能方面均优于生成对抗网络（GANs），能够生成多样化、解剖结构连贯且具有高视觉保真度的图像。与最先进的数据增强技术进行的对比分析表明，Med-DDPM可产生相当的结果，凸显其作为提升模型精度的数据增强工具的潜力。总之，Med-DDPM通过生成高质量且解剖结构连贯的图像，开创了三维语义医学图像合成的先河。此外，将语义条件机制与Med-DDPM相结合，有望在生物医学成像领域实现图像匿名化，展示了该模型在应对数据稀缺和隐私问题方面的能力。