# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 4 Discussion
It can also be seen from Table 7 that since the loss function is not calculated during the inference process, the use of clDice loss training does not affect the parameter number and inference \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **SE** & **SP** & **ACC** & **AUC** \\ \hline \multirow{4}{*}{DRIVE} & C-Branch & 0.7861 & 0.9431 & 0.9345 & 0.9353 \\  & MSC-Net (_wo_ CAS-FB) & 0.7894 & 0.9443 & 0.9358 & 0.9397 \\  & MSC-Net (_wo_ clDice) & 0.7806 & 0.9389 & 0.9483 & 0.9464 \\  & MSC-Net & **0.7905** & **0.9552** & **0.9510** & **0.9583** \\ \hline \multirow{4}{*}{STARE} & C-Branch & 0.8038 & 0.9545 & 0.9303 & 0.9455 \\  & MSC-Net (_wo_ CAS-FB) & 0.8214 & 0.9581 & 0.9358 & 0.9527 \\  & MSC-Net (_wo_ clDice) & 0.8467 & 0.9424 & 0.9321 & 0.9607 \\  & MSC-Net & **0.8546** & **0.9625** & **0.9423** & **0.9683** \\ \hline \multirow{4}{*}{CHASE} & C-Branch & 0.7675 & 0.8451 & 0.8905 & 0.9412 \\  & MSC-Net (_wo_ CAS-FB) & 0.7781 & 0.8661 & 0.9183 & 0.9508 \\ \cline{1-1}  & MSC-Net (_wo_ clDice) & 0.8047 & 0.8538 & 0.9178 & 0.9468 \\ \cline{1-1}  & MSC-Net & **0.8144** & **0.8865** & **0.9267** & **0.9541** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiments of the effects of different modules on centerline extraction. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **SE** & **SP** & **ACC** & **AUC** \\ \hline \multirow{4}{*}{DRIVE} & S-Branch & 0.7917 & 0.9362 & 0.9414 & 0.9422 \\  & MSC-Net (_wo_ CAS-FB) & 0.7917 & 0.9362 & 0.9587 & 0.9682 \\  & MSC-Net (_wo_ clDice) & 0.8268 & 0.9533 & 0.9528 & 0.9647 \\  & MSC-Net & **0.8423** & **0.9783** & **0.9663** & **0.9862** \\ \hline \multirow{4}{*}{STARE} & S-Branch & 0.8348 & 0.9684 & 0.9508 & 0.9674 \\  & MSC-Net (_wo_ CAS-FB) & 0.8684 & 0.9751 & 0.9654 & 0.9681 \\  & MSC-Net (_wo_ clDice) & 0.8578 & 0.9814 & 0.9477 & 0.9751 \\  & MSC-Net & **0.8763** & **0.9835** & **0.9713** & **0.9786** \\ \hline \multirow{4}{*}{CHASE} & S-Branch & 0.7817 & 0.9701 & 0.9503 & 0.9511 \\  & MSC-Net (_wo_ CAS-FB) & 0.7941 & 0.9681 & 0.9571 & 0.9582 \\ \cline{1-1}  & MSC-Net (_wo_ clDice) & **0.8145** & 0.9735 & 0.9583 & 0.9628. \\ \cline{1-1}  & MSC-Net & 0.8056 & **0.9869** & **0.9686** & **0.9714** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation experiments of the influence of different modules on vessel segmentation. time of the model (the subtle differences in the Table 7 are due to different experiments). Images of different sizes will also affect the network inference time: images with larger resolutions require more inference time. From the results of the inference time, it can be seen that MSC-Net has certain real-time performance and can be used for disease diagnosis, screening, treatment, and evaluation. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Resize** & **Method** & **Parameters (M)** & **Time (s)** \\ \hline \multirow{4}{*}{DRIVE} & \multirow{4}{*}{\(576\times 576\)} & S-Branch/C-Branch & 1.7682 & 0.0147 \\  & & MSC-Net (_two_ CAS-FB) & 4.2960 & 0.0197 \\  & & MSC-Net (_two_ cIDice) & 9.5251 & 0.0312 \\  & & MSC-Net & 9.5251 & 0.0326 \\ \hline \multirow{4}{*}{STARE} & \multirow{4}{*}{624\(\times\) 624} & S-Branch/C-Branch & 1.7682 & 0.0163 \\  & & MSC-Net (_two_ CAS-FB) & 4.2960 & 0.0211 \\  & & MSC-Net (_two_ cIDice) & 9.5251 & 0.0387 \\  & & MSC-Net & 9.5251 & 0.0362 \\ \hline \multirow{4}{*}{CHASE} & \multirow{4}{*}{960\(\times\) 960} & S-Branch/C-Branch & 1.7682 & 0.0367 \\  & & MSC-Net (_two_ cAS-FB) & 4.2960 & 0.0434 \\ \cline{1-1}  & & MSC-Net (_two_ cIDice) & 9.5251 & 0.0717 \\ \cline{1-1}  & & MSC-Net & 9.5251 & 0.0739 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The parameters and reasoning time when ablating different modules of the network. Figure 6: Visualization results after ablation of different modules. The first line of each case is the retinal image and vessel segmentation result, and the second line is the centerline result. The figure shows that the complete MSC-Net has better visualization results.