Abstract
Magnetic resonance (MR) imaging, including cardiac MR, is prone to domain shift due to variations in imaging devices and acquisition protocols. This challenge limits the deployment of trained AI models in real-world scenarios, where performance degrades on unseen domains. Traditional solutions involve increasing the size of the dataset through ad-hoc image augmentation or additional online training/transfer learning, which have several limitations. Synthetic data offers a promising alternative, but anatomical/structural consistency constraints limit the effectiveness of generative models in creating image-label pairs. To address this, we propose a diffusion model (DM) trained on a source domain that generates synthetic cardiac MR images that resemble a given reference. The synthetic data maintains spatial and structural fidelity, ensuring similarity to the source domain and compatibility with the segmentation mask. We assess the utility of our generative approach in multi-centre cardiac MR segmentation, using the 2D nnU-Net, 3D nnU-Net and vanilla U-Net segmentation networks. We explore domain generalisation, where, domain-invariant segmentation models are trained on synthetic source domain data, and domain adaptation, where, we shift target domain data towards the source domain using the DM. Both strategies significantly improved segmentation performance on data from an unseen target domain, in terms of surface-based metrics (Welch's t-test, p < 0.01), compared to training segmentation models on real data alone. The proposed method ameliorates the need for transfer learning or online training to address domain shift challenges in cardiac MR image analysis, especially useful in data-scarce settings.
Abstract (translated)
磁共振（MR）成像，包括心脏MR成像，在不同的成像设备和采集协议下容易发生领域偏移。这种挑战限制了训练好的AI模型在实际应用场景中的部署，因为它们在未见过的数据域上的性能会下降。传统的解决方案是通过人工图像增强或额外的在线训练/迁移学习来扩大数据集规模，但这几种方法存在一些局限性。合成数据为解决这一问题提供了一种有希望的替代方案，但由于解剖结构一致性约束的存在，生成模型在创建影像-标签对时的有效性受到了限制。 为了应对这些问题，我们提出了一种基于扩散模型（DM）的方法，该模型在一个源领域上进行训练，并能够根据给定的参考图像产生类似的心脏MR合成图像。这种方法产生的数据保持了空间和结构的一致性，确保与原数据域相似且与分割掩模兼容。我们在多中心心脏MR分割任务中评估了我们生成方法的有效性，使用了2D nnU-Net、3D nnU-Net以及普通的U-Net分割网络。 我们的研究探索了两种策略：领域泛化和领域适应。在领域泛化中，我们将不变的分割模型训练于合成源域数据上；而在领域适应中，我们利用扩散模型将目标域数据向源域转换。这两种方法均显著提高了对未见目标域数据的分割性能（基于Welch's t-test, p < 0.01）。与仅在真实数据上训练分割模型相比，这些策略在表面度量指标方面表现出明显优势。 所提出的这种方法改善了处理心脏MR图像分析中的领域偏移问题的需求，特别是在数据匮乏的情况下，减少了迁移学习或在线培训的必要性。
URL
https://arxiv.org/abs/2508.06327
