## 5 Results
### Ablation Study
#### 5.2.2 Efficacy of DR Reconstruction Branch  
To illustrate the efficacy of the proposed DR reconstruction branch, we integrated it with the baseline in the experiment. As shown in Table 4, the addition of the DR reconstruction branch led to improvements across all evaluation metrics on the DRIVE dataset  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Acc** & **AUC** & **Spe** & **Sen** \\ \hline Baseline & 0.9746 & 0.9689 & 0.9884 & 0.7656 \\ Baseline + MSFE & 0.9765 & 0.9774 & 0.9868 & 0.8198 \\ Baseline + DR & 0.9778 & 0.9783 & 0.9885 & 0.8169 \\ Baseline + MSFE + DR & 0.9765 & 0.9811 & 0.9862 & 0.8296 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation results on the STARE dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Acc** & **AUC** & **Spe** & **Sen** \\ \hline Baseline & 0.9626 & 0.9751 & 0.9771 & 0.8052 \\ Baseline + MSFE & 0.9680 & 0.9810 & 0.9807 & 0.8213 \\ Baseline + DR & 0.9680 & 0.9829 & 0.9802 & 0.8207 \\ Baseline + MSFE + DR & 0.9690 & 0.9858 & 0.9829 & 0.8230 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation results on the DRIVE dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Acc** & **AUC** & **Spe** & **Sen** \\ \hline Baseline & 0.9743 & 0.9723 & 0.9836 & 0.8090 \\ Baseline + MSFE & 0.9754 & 0.9752 & 0.9849 & 0.8254 \\ Baseline + DR & 0.9747 & 0.9775 & 0.9839 & 0.8179 \\ Baseline + MSFE + DR & 0.9757 & 0.9808 & 0.9858 & 0.8263 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation results on the CHASE_DB1 dataset.  
(accuracy: 0.9626/0.9680; AUC: 0.9751/0.9829; specificity: 0.9771/0.9802; and sensitivity: 0.8052/0.8207). The most significant increases were observed in the AUC and sensitivity values, with the AUC increasing by 0.0078 and sensitivity increasing by 0.0155. In our network, the purpose of the DR reconstruction branch is to encourage the segmentation branch to focus more on spatial information and suppress redundant information, which is beneficial to the segmentation task and thus can effectively improve the network's segmentation performance. Therefore, the accuracy and AUC values should increase after the introduction of the DR reconstruction branch, which is fully proven by the experimental results on the DRIVE dataset. Similarly, the results of the ablation experiments on the CHASE_DB1 and STARE datasets also validate the effectiveness of the DR reconstruction branch. As shown in Table 5, by adding the DR reconstruction branch, the accuracy, AUC, specificity, and sensitivity values all increased. The sensitivity improved from 0.8090 to 0.8179, while the specificity was unaffected. This outcome suggests that the DR reconstruction branch effectively enhances the model's capacity to accurately segment vessel pixels without compromising its ability to detect non-vessel pixels. For the STARE dataset, the network combined with the DR reconstruction branch outperformed the baseline across all four metrics, reaching up to 0.9778, 0.9783, 0.9885, and 0.8169. As depicted in Tables 4-6, the adoption of the DR reconstruction branch resulted in improved values for accuracy, AUC, specificity, and sensitivity across all datasets, which indicates that the performance gain achieved by the DR reconstruction branch is significant.