This paper introduces Med-DDPM, an innovative solution using diffusion models
for semantic 3D medical image synthesis, addressing the prevalent issues in
medical imaging such as data scarcity, inconsistent acquisition methods, and
privacy concerns. Experimental evidence illustrates that diffusion models
surpass Generative Adversarial Networks (GANs) in stability and performance,
generating high-quality, realistic 3D medical images. The distinct feature of
Med-DDPM is its use of semantic conditioning for the diffusion model in 3D
image synthesis. By controlling the generation process through pixel-level mask
labels, it facilitates the creation of realistic medical images. Empirical
evaluations underscore the superior performance of Med-DDPM over GAN techniques
in metrics such as accuracy, stability, and versatility. Furthermore, Med-DDPM
outperforms traditional augmentation techniques and synthetic GAN images in
enhancing the accuracy of segmentation models. It addresses challenges such as
insufficient datasets, lack of annotated data, and class imbalance. Noting the
limitations of the Frechet inception distance (FID) metric, we introduce a
histogram-equalized FID metric for effective performance evaluation. In
summary, Med-DDPM, by utilizing diffusion models, signifies a crucial step
forward in the domain of high-resolution semantic 3D medical image synthesis,
transcending the limitations of GANs and data constraints. This method paves
the way for a promising solution in medical imaging, primarily for data
augmentation and anonymization, thus contributing significantly to the field.

该论文介绍了 Med-DDPM，这是一种使用扩散模型进行语义 3D 医学图像合成的创新解决方案，解决了医学成像中的普遍问题，如数据稀缺、不一致的采集方法和隐私问题。