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. Our code and model weights are publicly accessible on our GitHub repository at https://github.com/mobaidoctor/med-ddpm/, facilitating reproducibility.

翻译：医疗健康领域对人工智能（AI）的需求正在迅速增长。然而，数据稀缺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已取得成功，但在三维语义医学图像的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3D医学图像合成设计的扩散模型，有效解决了数据稀缺和隐私问题。Med-DDPM的创新之处在于其整合了语义条件控制，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上均优于生成对抗网络（GANs），能够生成多样且解剖结构连贯的高视觉保真度图像。与最先进的数据增强技术进行的对比分析表明，Med-DDPM取得了相当的结果，凸显了其作为数据增强工具提升模型准确性的潜力。总之，Med-DDPM通过生成高质量且解剖结构连贯的图像，开创了语义3D医学图像合成的先河。此外，Med-DDPM与语义条件控制的结合在生物医学成像领域具有图像匿名化的前景，展示了该模型应对数据稀缺和隐私问题挑战的能力。我们的代码和模型权重已在GitHub仓库（https://github.com/mobaidoctor/med-ddpm/）中公开发布，以促进可重复性研究。