###### Abstract
The method introduced above was based on the traditional convolutional neural network. The current mainstream was a symmetrical Fully Convolutional Networks (FCN) network for retinal vessel segmentation called U-Net [18], which had achieved good results in the field of retinal vessel segmentation. On the basis of U-Net, [PERSON] et al. [19] proposed a new lightweight retinal vessel segmentation model called Vessel-Net, which added an efficient initial residual convolution block to the structure of U-Net. The model combined the advantages of U-Net and residual module to improve feature representation, which greatly improved the segmentation performance. [PERSON] et al. [20] introduced a new curvilinear structure segmentation network (CS2-Net), which included a self-attention mechanism in the encoder and decoder. The two attention modules were used to enhance inter-class discrimination and inter-class responsiveness. Finally, experiments were carried out on datasets to verify the effectiveness of the method. [PERSON] et al. [21] proposed an advanced confrontational training method to defend against the interference of unknown confrontational examples. The author used the Fast Gradient Sign Method (FGSM) to generate a fixed range of value of the adversarial example for training. The experiments showed that the U-Net model trained on different adversarial examples was more robust to unknown adversarial examples. [PERSON] et al. [22] proposed a Multi-Scale Convolutional Neural Network with Attention Mechanisms (MSCNN-AM). The model introduced various separable convolutions with varying dilation rates, which could capture global and multi-scale vessel information better. Meanwhile, in order to reduce false-positive predictions for tiny vessel pixels, the author also adopted attention mechanisms so that the proposed MSCNN-AM could pay more attention to retinal vessel pixels instead of background pixels, and experiments showed that the model achieved good results. In [23], the author proposed a Multiscale Channel Attention Network based on the encoder-decoder architecture for fundus retinal vessel segmentation, which redesigned encoder extracts the multi-scale structure information of retinal vessels by fusing multi-scale features, which weakened the influence of complex morphology on segmentation performance. The results on the DRIVE dataset and the CHASE_DB1 dataset showed that the network could segmented the capillaries in fundus retinal images well. So far, the network evolved from U-Net dominated the field of retinal blood vessel segmentation, but they also had certain limitations. Specifically, U-Net divides the fundus retinal image into image blocks, and then inputs them into the network. It does not carefully extract the features of each pixel, so it loses the detailed features in the retinal blood image. In order to solve this problem, this paper proposes a multiple multi-scale neural network knowledge transfer and integration method for accurate pixel-level retinal blood vessel segmentation. This method has the following characteristics: (1) it can fuse different retinal features with different sizes and different depth via multiple multi-scale neural networks to obtain better segmented results; (2) using the knowledge transfer avoids excessively long network training; (3) using networks integration to get better segmented results. Combining the above three characteristics, the segmentation effect can be improved effectively. The remainder of the paper is organized as follows: Section 2 introduces the details of the proposed method; experimental results are reported in Section 3; in Sections 4 and 5, discussions and conclusions are presented.