## 5 Conclusions  
This paper proposes a multiple multi-scale neural networks knowledge transfer and integration method for accurate pixel-level retinal blood vessel segmentation. It can fuse different retinal features with different scale sizes and different networks and use the knowledge transfer to enhance the segmentation results. Experiments in the DRIVE and CHASE_DB1 datasets were used to verify the effectiveness of the proposed method in retinal vascular segmentation.  
**Author Contributions:** All of the authors made significant contributions to this work. [PERSON], [PERSON] and [PERSON] devised the approach and analyzed the data; [PERSON], [PERSON] and [PERSON] helped design the experiments and provided advice for the preparation and revision of the work; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] wrote this manuscript. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epochs** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline
1 & 96.34 & 75.57 & 98.30 \\
10 & **96.43** & **78.00** & 98.23 \\
20 & 96.34 & 76.62 & 98.26 \\
30 & 96.31 & 77.63 & 98.14 \\
40 & 96.29 & 75.86 & 98.28 \\
50 & 96.32 & 76.27 & 98.28 \\
60 & 96.30 & 77.11 & 98.17 \\
70 & 96.30 & 76.27 & 98.25 \\
80 & 96.31 & 76.66 & 96.23 \\
90 & 96.31 & 76.17 & 98.28 \\
100 & 96.35 & 75.61 & **98.37** \\ \hline \hline \end{tabular}
\end{table}
Table 18: The influence of training epochs in the DRIVE dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epochs** & **Acc (\%)** & **SE (\%)** & **SP (\%)** \\ \hline
1 & 97.05 & 75.08 & 98.73 \\
10 & **97.08** & **75.43** & 98.69 \\
20 & 97.06 & 73.58 & 98.81 \\
30 & 97.06 & 74.12 & 98.76 \\
40 & 97.07 & 74.33 & 98.76 \\
50 & 97.06 & 73.47 & 98.81 \\
60 & 97.04 & 75.12 & 98.68 \\
70 & 97.03 & 73.51 & 98.79 \\
80 & 97.08 & 74.05 & 98.79 \\
90 & 97.04 & 74.23 & 98.80 \\
100 & 97.07 & 73.09 & **98.88** \\ \hline \hline \end{tabular}
\end{table}
Table 19: The influence of training epochs in the CHASE_DB1 dataset.  
**Funding:** This work was supported by the National Natural Science Foundations of China (grant no. 61901369, grant no. 62071387 and grant no. 62101454), the Foundation of National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology (grant no. 20200203) and the National Key Research and Development Project of China (no. 2020 AAA0104603).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** DRIVE can be found at [[https://drive.grand-challenge.org/Download/](https://drive.grand-challenge.org/Download/)]([https://drive.grand-challenge.org/Download/](https://drive.grand-challenge.org/Download/)). CHASE_DB1 can be found at [[https://blogs.kingston.ac.uk/retinal/chasedb1/](https://blogs.kingston.ac.uk/retinal/chasedb1/)]([https://blogs.kingston.ac.uk/retinal/chasedb1/](https://blogs.kingston.ac.uk/retinal/chasedb1/)).  
**Acknowledgments:** We acknowledge the author for the DRIVE and CHASE_DB1 datasets.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.