Deep models suffer from limited generalization capability to unseen domains, which has severely hindered their clinical applicability. Specifically for the retinal vessel segmentation task, although the model is supposed to learn the anatomy of the target, it can be distracted by confounding factors like intensity and contrast. We propose Meta learning on Anatomy-consistent Pseudo-modalities (MAP), a method that improves model generalizability by learning structural features. We first leverage a feature extraction network to generate three distinct pseudo-modalities that share the vessel structure of the original image. Next, we use the episodic learning paradigm by selecting one of the pseudo-modalities as the meta-train dataset, and perform meta-testing on a continuous augmented image space generated through Dirichlet mixup of the remaining pseudo-modalities. Further, we introduce two loss functions that facilitate the model's focus on shape information by clustering the latent vectors obtained from images featuring identical vasculature. We evaluate our model on seven public datasets of various retinal imaging modalities and we conclude that MAP has substantially better generalizability. Our code is publically available at https://github.com/DeweiHu/MAP.

深度模型在应用于未知领域时往往存在泛化能力有限的问题，特别是在视网膜血管分割任务中，我们提出了一种基于解剖一致伪模态的元学习方法（MAP），通过学习结构特征来提高模型的泛化能力。我们首先利用特征提取网络生成三个不同的伪模态，这些模态与原始图像共享血管结构。然后，我们使用情节学习范式，将其中一个伪模态作为元训练数据集，并通过对其余伪模态进行Dirichlet混合生成连续增强的图像空间进行元测试。此外，我们引入了两个损失函数，通过对具有相同血管结构的图像获取的潜在向量进行聚类来促进模型对形状信息的关注。我们在包括多种视网膜成像模态的七个公共数据集上评估了我们的模型，并得出结论：MAP具有更好的泛化能力。我们的代码在此https URL公开可用。

基于解剖一致的伪模态元学习的领域泛化