###### Abstract  
Retinal blood vessel segmentation plays an important role for analysis of retinal diseases, such as diabetic retinopathy and glaucoma. However, retinal blood vessel segmentation remains a challenging task due to the low contrast between some vessels and background, the different presenting conditions caused by uneven illumination and the artificial segmentation results are influenced by human experience, which seriously affects the classification accuracy. To address this problem, we propose a multiple multi-scale neural networks knowledge transfer and integration method in order to accurately segment for retinal blood vessel image. With the integration of multi-scale networks and multi-scale input patches, the blood vessel segmentation performance is obviously improved. In addition, applying knowledge transfer to the network training process, the pre-trained network reduces the number of network training iterations. The experimental results on the DRIVE dataset and the CHASE_DB1 dataset show the effectiveness of the method, whose average accuracy on the two datasets are 96.74% and 97.38%, respectively. 1 2  
The machine learning-based retinal vessel segmentation methods can be always divided into deep learning-based methods and non-deep learning-based methods. Among non-deep learning methods, it was the first proposed to use a Gaussian filter to segment the blood vessel image, which used the features of the blood vessel to solve the difficulties for segmentation, e.g., low contrast of the local blood vessel [8]. A B-Combination of Shifted Filter Responses (COSFIRE) filter [9] was proposed with selective response to automatically segment blood vessel trees and this filter achieved good results. Based on the above, the authors presented a segmentation method combining multi-scale matched filtering and double-threshold, which could reduce the influence of background noise [10]. Except for the filter-based method, the authors proposed a segmentation method with iterative adaptive threshold, which iteratively discovered new vascular pixels through global threshold of enhanced images and adaptive threshold of residual images [11]. In [12], the authors presented a K-Nearest Neighbor (KNN)-based approach, which used sequential forward feature selection to distinguish vascular and non-vascular. In [13], a retinal vessel segmentation method based on morphological component analysis (MCA) was proposed to solve the problem of false positive vessels. However, the non-deep learning-based retinal vessel segmentation methods often relied on empirical feature extraction and led to poor segmentation effect. For this reason, deep learning-based methods were proposed to be used for segmentation. In order to enhance the segmentation effect, more and more deep learning-based methods had been proposed in the field of retinal vascular segmentation. The method used deep neural network to segment retinal blood vessels, which was pre-processed by global contrast normalization, zero phase whitening, geometric transformation and Gamma calibration data, and it had good resistance to central vascular reflex phenomenon and could segment blood vessels well [14]. [PERSON] et al. [15] proposed a Bottom-top short connections deeply supervised network (BTS-DSN) method, which could transmit semantic information and structural information of retinal images through a short connection neural network. In [16], the author proposed a retinal blood vessel segmentation method that combined Convolutional Neural Networks (CNN) quantization and pruning. In [16], quantitative technology was used in the fully connected layer, and the pruning strategy was used in the convolutional layer to form an effective network and improve the accuracy of segmentation. [PERSON] et al. [17] employed SCS-Net (Scale and Context Sensitive Network), which designed an adaptive feature fusion (AFF) module to guide efficient fusion between adjacent hierarchical features to capture more semantic information.