# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Results  
The vessel segmentation results of MSC-Net and five state-of-the-art methods on three retinal datasets are compared to evaluate the effectiveness of MSC-Net. The methods for comparison, including traditional segmentation methods, are based on matched filters, namely Combination of Shifted Filter Responses (B-COSFIRE) [6] and Weighted Symmetry Filter (WSF) [7], as well as deep learning methods, namely U-Net [17], R2U-net [18], and CS\({}^{2}\)-Net [20]. Table 3 shows the vessel segmentation metrics of different methods on the DRIVE, STARE, and CHASE databases. The results of B-COSFIRE and WSF are quoted from their papers. Based on Table 3, our proposed MSC-Net outperform COSFIRE, WSF, U-Net, and R2U-Net in terms of ACC and AUC scores, and also has competitive SE and SP scores. In addition, Figure 4 shows the visualization results of different methods. Image instances are randomly selected in the three datasets. Figure 4 shows the retinal image, ground truth, and segmentation results of various methods for each image instance. The displayed segmentation results include U-Net, R2U-Net, CS\({}^{2}\)-Net, and our proposed MSC-Net. Figure 4 shows that our method has better continuity and fewer branch breaks compared with other methods. MSC-Net also obtains segmentation results that are closer to the ground truth in some details because of poor contrast or noise. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline \(\alpha\) & 0.4 \\ \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) & 0.2, 0.5, and 0.1 \\ batchsize & 8 \\ learning rate & \(1\times 10^{-3}\) \\ epochs & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: This table describes the detailed setting values of different parameters during implementation. The \(\alpha\) is the parameter in \(\mathcal{L}oss_{cline}\) (see Equation (10)), and \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) are hyper-parameters in \(\mathcal{L}oss\) (see Equation (14)). The settings of batchsize, learning rate, and epoch during training stage are also shown in the table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **SE** & **SP** & **ACC** & **AUC** \\ \hline \multirow{6}{*}{DRIVE} & B-COSFIRE [6] & 0.7526 & 0.9707 & 0.9427 & 0.9514 \\  & WSF [7] & 0.7740 & 0.9790 & 0.9580 & 0.9750 \\  & U-Net [17] & 0.7817 & 0.9759 & 0.9531 & 0.9622 \\  & R2U-Net [18] & 0.7992 & 0.9712 & 0.9556 & 0.9681 \\  & CS\({}^{2}\)-Net [20] & 0.8259 & **0.9850** & 0.9622 & 0.9763 \\  & MSC-Net & **0.8423** & 0.9783 & **0.9663** & **0.9862** \\ \hline \multirow{6}{*}{STARE} & B-COSFIRE [6] & 0.7543 & 0.9689 & 0.9467 & 0.9487 \\  & WSF [7] & 0.7880 & 0.9760 & 0.9570 & 0.9590 \\  & U-Net [17] & 0.7956 & 0.9764 & 0.9578 & 0.9617 \\  & R2U-Net [18] & 0.8488 & 0.9754 & 0.9618 & 0.9659 \\  & CS\({}^{2}\)-Net [20] & 0.8516 & 0.9748 & 0.9651 & 0.9727 \\  & MSC-Net & **0.8763** & **0.9835** & **0.9713** & **0.9786** \\ \hline \multirow{6}{*}{CHASE} & B-COSFIRE [6] & 0.7257 & 0.9651 & 0.9411 & 0.9434 \\  & WSF [7] & - & - & - & - \\ \cline{1-1}  & U-Net [17] & 0.7931 & 0.9793 & 0.9480 & 0.9464 \\ \cline{1-1}  & R2U-Net [18] & **0.8062** & 0.9779 & 0.9457 & 0.9530 \\ \cline{1-1}  & CS\({}^{2}\)-Net [20] & 0.7841 & 0.9831 & 0.9522 & 0.9628 \\ \cline{1-1}  & MSC-Net & 0.8056 & **0.9869** & **0.9686** & **0.9714** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SE, SP, ACC, and AUC scores of MSC-Net and other baseline methods for vessel segmentation task on the test set. We also compare the centerline results with other methods on the three retinal fundus datasets. The comparison methods include skeletonization (Skeleton) [28] and U-Net [17]. The skeletonization method directly generates the centerline from the segmentation result of MSC-Net. The processing method is the same as the method used to generate the centerline label from the ground truth of the vessel. Table 4 shows the centerline scores of different methods.