## 6 Conclusions and Future Work  
This paper proposes a de-normalized channel attention network based on a dual U-Net structure. De-normalized blocks and channel attention modules were added to accurately segment sophisticated blood vessels such as retinal blood vessels and brain blood vessels, and our experiments have achieved superior results. To test the impact of the de-normalized block on the segmentation network, we examined the effects using batch normalization and de-normalization on three data sets and compared them. Experiments show that our de-normalized block can improve network performance and robustness while achieving higher accuracy. In the next step, we will continue to refine our segmentation network and apply the segmentation results to the vascular navigation path planning.  
Methodology, D.H., L.Y., H.G., W.T. and T.R.W.; software, D.H., L.Y. and H.G.; writing--original draft preparation, D.H., L.Y., H.G., W.T. and T.R.W.; writing--review and editing, D.H., L.Y., T.R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Natural Science Foundation of China grant number 19 ZR1419100, the National Natural Science Foundation of China grant number 61402278.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Dataset** & **Year** & **Methods** & **Accuracy** & **Precision** & **F\({}_{\beta}\)-Score** & **AUC** \\ \hline \multirow{8}{*}{DRIVE} & 2015 & U-net [3] & 0.9531 & **0.8852** & 0.8142 & 0.9755 \\  & 2018 & Recurrent U-Net [15] & 0.9556 & 0.8603 & 0.8155 & 0.9782 \\  & 2018 & Residual U-Net [15] & 0.9553 & 0.8614 & 0.8149 & 0.9779 \\  & 2018 & R2U-Net [15] & 0.9556 & 0.8589 & 0.8171 & 0.9784 \\  & 2018 & LadderNet [16] & 0.9561 & 0.8593 & 0.8202 & 0.9793 \\  & 2019 & Dynamic Deep Networks [17] & 0.9693 & 0.8284 & 0.8259 & 0.9775 \\  & 2020 & IterNet(Patched) [18] & 0.9573 & 0.8534 & 0.8205 & 0.9816 \\  & 2020 & FAU-net & **0.9698** & 0.8651 & **0.8320** & **0.9853** \\ \hline \multirow{8}{*}{ELE} & 2015 & U-net & 0.9801 & 0.9631 & 0.9486 & 0.9875 \\  & 2018 & Recurrent U-Net & 0.9776 & **0.9736** & 0.9465 & 0.9930 \\  & 2018 & Residual U-Net & 0.9837 & 0.9670 & 0.9582 & 0.9902 \\  & 2018 & R2U-Net & 0.9806 & 0.9501 & 0.9684 & 0.9955 \\  & 2018 & LadderNet & 0.9889 & 0.9709 & 0.9685 & 0.9980 \\  & 2019 & Dynamic Deep Networks & 0.9816 & 0.9435 & 0.9684 & 0.9942 \\  & 2020 & IterNet(Patched) & 0.9821 & 0.9664 & 0.9568 & 0.9961 \\  & 2020 & FAU-net & **0.9891** & 0.9695 & **0.9688** & **0.9982** \\ \hline \multirow{8}{*}{CORO} & 2015 & U-net & 0.9801 & 0.9784 & 0.9633 & 0.9808 \\  & 2018 & Recurrent U-Net & 0.9739 & 0.9806 & 0.9541 & 0.9912 \\ \cline{1-1}  & 2018 & Residual U-Net & 0.9716 & 0.9792 & 0.9498 & 0.9755 \\ \cline{1-1}  & 2018 & R2U-Net & 0.9786 & 0.9892 & 0.9642 & 0.9893 \\ \cline{1-1}  & 2018 & LadderNet & **0.9843** & 0.9906 & 0.9723 & **0.9962** \\ \cline{1-1}  & 2019 & Dynamic Deep Networks & 0.9798 & 0.9758 & 0.9649 & 0.9897 \\ \cline{1-1}  & 2020 & IterNet(Patched) & 0.9739 & 0.9825 & 0.9654 & 0.9953 \\ \cline{1-1}  & 2020 & FAU-net & 0.9834 & **0.9932** & **0.9730** & 0.9928 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of FAU-net compared with other networks on DRIVE, ELE, CORO data sets.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **DRIVE** & **ELE** & **CORO** \\ \hline U-Net & 2.16 s & 2.35 s & 2.05 s \\ Recurrent U-Net & 5.23 s & 4.56 s & 4.23 s \\ Residual U-Net & 3.57 s & 4.16 s & 3.13 s \\ R2U-Net & 5.75 s & 2.54 s & 5.34 s \\ IterNet (Patched) & 16.53 s & 14.45 s & 15.86 s \\ LadderNet & 1.78 s & 2.01 s & 1.41 s \\ Dynamic Deep Networks & 4.72 s & 4.23 s & 3.88 s \\ FAU-Net & 1.43 s & 1.27 s & 1.21 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: computational time for testing(Time (s)/sample).