Recent advances in computer vision have shown promising results in image generation. Diffusion probabilistic models in particular have generated realistic images from textual input, as demonstrated by DALL-E 2, Imagen and Stable Diffusion. However, their use in medicine, where image data typically comprises three-dimensional volumes, has not been systematically evaluated. Synthetic images may play a crucial role in privacy preserving artificial intelligence and can also be used to augment small datasets. Here we show that diffusion probabilistic models can synthesize high quality medical imaging data, which we show for Magnetic Resonance Images (MRI) and Computed Tomography (CT) images. We provide quantitative measurements of their performance through a reader study with two medical experts who rated the quality of the synthesized images in three categories: Realistic image appearance, anatomical correctness and consistency between slices. Furthermore, we demonstrate that synthetic images can be used in a self-supervised pre-training and improve the performance of breast segmentation models when data is scarce (dice score 0.91 vs. 0.95 without vs. with synthetic data). The code is publicly available on GitHub: https://github.com/FirasGit/medicaldiffusion.
翻译:计算机视觉方面的近期进步显示了在图像生成方面的可喜结果。特别是扩散概率模型从文本输入中产生了现实的图像,DALL-E 2,图像和稳定扩散就证明了这一点。然而,在医学中,图像数据通常由三维体积组成,但还没有系统地评估这些图像在医学中的使用情况。合成图像在隐私保护人工智能方面可以发挥关键作用,也可以用来增加小数据集。我们在这里表明,扩散概率模型可以综合高质量的医学成像数据,我们在磁共振成像(MRI)和复合成像(CT)图像中展示了这些数据。我们通过由两名医学专家进行的读者研究提供其性能的定量测量,这些医学专家对合成图像的质量进行了分为三类的评级:真实图像的外观、解剖的正确性和切片之间的一致性。此外,我们证明合成图像可以在自我超超超前训练中使用,并在数据稀少时改进乳分模型的性能(dice 评分0.91比s.0.95,没有合成数据)。我们通过对合成数据进行阅读来进行定量测量。代码可以公开查阅:GiH/Gmbs/comliflifismission:http:http:http://http://Gibgibs/Gibs/Gmb/Gismillif)。