Blessed by vast amounts of data, learning-based methods have achieved remarkable performance in countless tasks in computer vision and medical image analysis. Although these deep models can simulate highly nonlinear mapping functions, they are not robust with regard to the domain shift of input data. This is a significant concern that impedes the large-scale deployment of deep models in medical images since they have inherent variation in data distribution due to the lack of imaging standardization. Therefore, researchers have explored many domain generalization (DG) methods to alleviate this problem. In this work, we introduce a Hessian-based vector field that can effectively model the tubular shape of vessels, which is an invariant feature for data across various distributions. The vector field serves as a good embedding feature to take advantage of the self-attention mechanism in a vision transformer. We design paralleled transformer blocks that stress the local features with different scales. Furthermore, we present a novel data augmentation method that introduces perturbations in image style while the vessel structure remains unchanged. In experiments conducted on public datasets of different modalities, we show that our model achieves superior generalizability compared with the existing algorithms. Our code and trained model are publicly available at .

中文翻译：

---

基于 Hessian 向量场的视网膜血管分割的域泛化


得益于大量数据，基于学习的方法在计算机视觉和医学图像分析的无数任务中取得了卓越的性能。尽管这些深度模型可以模拟高度非线性的映射函数，但它们对于输入数据的域移位并不鲁棒。这是一个严重的问题，阻碍了医学图像中深度模型的大规模部署，因为由于缺乏成像标准化，它们在数据分布上存在固有的变化。因此，研究人员探索了许多领域泛化（DG）方法来缓解这个问题。在这项工作中，我们引入了一种基于 Hessian 的向量场，它可以有效地模拟血管的管状形状，这是跨各种分布的数据的不变特征。矢量场作为一个很好的嵌入特征来利用视觉转换器中的自注意力机制。我们设计了并行的变压器块，强调不同尺度的局部特征。此外，我们提出了一种新颖的数据增强方法，该方法在血管结构保持不变的情况下引入图像风格的扰动。在对不同模态的公共数据集进行的实验中，我们表明，与现有算法相比，我们的模型具有出色的通用性。我们的代码和经过训练的模型可在 上公开获取。