Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for data augmentation can improve segmentation accuracy, particularly in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https: //github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

本研究解决了医疗影像中深度学习模型在图像采集参数变化下的泛化性和鲁棒性问题。我们提出了一种使用条件去噪扩散生成模型生成反事实磁共振图像的新方法，此方法能在不改变患者解剖结构的情况下模拟不同的图像采集参数。研究表明，使用这些反事实图像进行数据增强可以提高分割精度，尤其是在分布外的设置中，从而增强深度学习模型在多样化影像条件下的泛化能力和鲁棒性。

使用条件去噪扩散生成模型进行反事实MRI数据增强