Domain Generalization for Retinal Vessel Segmentation with Vector Field Transformer

Abstract

Domain generalization has great impact on medical image analysis as data distribution inconsistencies are prevalent in most of the medical data modalities due to the image acquisition techniques. In this study, we investigate a novel pipeline that generalizes the retinal vessel segmentation across color fundus photography and OCT angiography images. We hypothesize that the scaled minor eigenvector of the Hessian matrix can sufficiently represent the vessel by vector flow. This vector field can be regarded as a common domain for different modalities as it is very similar even for data that follows vastly different intensity distributions. Next, we leverage the uncertainty in the latent space of the auto-encoder to synthesize enhanced vessel maps to augment the training data. Finally, we propose a transformer network to extract features from the vector field. We show the performance of our model in cross-modality experiments.