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).
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。传播概率模型尤其从文本输入中产生了现实的图像,DALL-E 2,图像和稳定扩散就是证明。然而,在医学中,图像数据通常由三维体积组成,但医学中的使用却没有得到系统的评估。合成图像在保护隐私方面可以发挥关键作用,也可以用来增加小数据集。在这里,我们表明,扩散概率模型可以合成高质量的医学成像数据,我们在磁共振成像(MRI)和Comput Tomagraphy(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,这些专家对合成图像分为三类:真实图像的外观、解剖正确性和切片之间的一致性。此外,我们证明合成图像可以用于自我超强的训练前阶段,并在数据稀少时改进乳分化模型的性能(dice 评分0.91比0.95,没有合成数据)。