Dual-Spatial Domain Generalization for Fundus Lesion Segmentation in Unseen Manufacturer's OCT Images

Objective: Optical Coherence Tomography (OCT) images can provide non-invasive visualization of fundus lesions; however, scanners from different OCT manufacturers largely vary from each other, which often leads to model deterioration to unseen OCT scanners due to domain shift. Methods: To produce the T-styles of the potential target domain, an Orthogonal Style Space Reparameterization (OSSR) method is proposed to apply orthogonal constraints in the latent orthogonal style space to the sampled marginal styles. To leverage the high-level features of multi-source domains and potential T-styles in the graph semantic space, a Graph Adversarial Network (GAN) is constructed to align the generated samples with the source domain samples. To align features with the same label based on the semantic feature in the graph semantic space, Graph Semantic Alignment (GSA) is performed to focus on the shape and the morphological differences between the lesions and their surrounding regions. Results: Comprehensive experiments have been performed on two OCT image datasets. Compared to state-of-the-art methods, the proposed method can achieve better segmentation. Conclusion: The proposed fundus lesion segmentation method can be trained with labeled OCT images from multiple manufacturers' scanners and be tested on an unseen manufacturer's scanner with better domain generalization.