The morphological structure of retinal fundus blood vessels is of great significance for medical diagnosis, thus the automatic retinal vessel segmentation algorithm has become one of the research hotspots in the field of medical image processing. However, there are still several unsolved difficulties in this task: the existed methods are too sensitive to the low-frequency noise in the fundus images, and there are few annotated data sets available, and meanwhile, the retinal images of different datasets vary greatly. To solve the above problems, we propose a domain adaptive vessel segmentation algorithm with multiple image entrances called MIUnet, which is robust to the etiological noises and domain shift between diverse datasets. We apply Fourier domain adaptation and the high-frequency component filtering modules to transform the raw images into two styles, and simultaneously reduce the discrepancy between the source domain and target domain retinal images. After that, images produced by the two modules are fed into a multi-input deep segmentation model, and the full utilization of features from different modalities is ensured by the deep supervision mechanism. Experiments prove that, compared with other segmentation methods, the MIUnet has better performances in cross-domain experiments, where the IoU reaches 63% when trained on ARIA dataset and tested on the DRIVE dataset and 53% in the opposite direction.