# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 4 Discussion  
MSC-Net is designed from the following aspects to achieve accurate retinal vessel segmentation and centerline extraction at the same time. (1) The tasks of blood vessel segmentation and centerline extraction are related to each other; thus, blood vessels can limit the region of the centerline, and the centerline can supplement the small parts of the blood vessels. Therefore, a multitask learning framework is proposed, which can simultaneously perform retinal vessel segmentation and centerline extraction. (2) CAS-FB is designed for feature fusion and calibration. Channel attention can perform feature correction between different tasks, and the feature fusion block of multiscale spatial attention can effectively fuse the features of different sizes of blood vessels. (3) The framework is trained with clDice loss function, which can achieve mutual constraint on the segmentation and centerline extraction results to ensure topological consistency. The indicators of different ablation methods are further calculated on three fundus datasets (DRIVE, STARE, and CHASE), to prove the effectiveness of these three designs (Tables 5 and 6). The specific methods are as follows:  
* S-Branch/C-Branch: The segmentation (S)/centerline (C) branch in MSC-Net is trained separately. This method is equivalent to performing only one task to achieve blood vessel segmentation or centerline extraction. * MSC-Net (_wo_ CAS-FB): The CAS-FB module in MSC-Net is replaced with a fusion block. The fusion block only concatenates the features of the two branches and uses a convolutional layer, BN, and ReLU layer for feature fusion. * MSC-Net (_wo_ clDice): When training MSC-Net, the clDice loss function is not added to the loss function (equivalent to the \(\lambda_{3}\) = 0 in Equation (14)). * MSC-Net: The result of training on our proposed MSC-Net. Tables 5 and 6 compare the S-Branch/C-Branch and the other methods using multitask learning. The results showed that the use of multitask methods can achieve better performance in blood vessel segmentation and centerline extraction. Compared with MSC-Net (_wo_ CAS-FB), MSC-Net with CAS-FB for feature fusion has the best performance in all indicators. This finding proves that the use of channel and multiscale spatial attention  
Figure 5: Visualization results of different centerline extraction methods. The centerline extracted by our method will have better continuity. is very effective for feature fusion and is more suitable for a target with large changes in feature morphology, such as blood vessels. Compared with the result of MSC-Net (_wo_ clDice), the use of clDice loss function improved the performance of MSC-Net. Figure 6 shows the visualization results of the influence of CAS-FB and clDice on the topological continuity of blood vessel segmentation and centerline extraction. The figure shows that MSC-Net with CAS-FB and clDice has better topological continuity and less interruption than MSC-Net (_two_ CAS-FB) and MSC-Net (_wo_ clDice) in predicting vessel segmentation and centerline extraction. In short, MSC-Net combines blood vessel segmentation and centerline extraction by using a multitask learning method, which can achieve better results than a single task. The feature fusion path composed of channels and spatial attention modules can solve the calibration and fusion of multitask and multiscale features. Using clDice loss function to constrain blood vessels and centerlines can effectively improve the accuracy of blood vessel segmentation and centerline extraction and ensure that the results are more in line with ground truth and topological consistency. To further explore the influence of different modules on the number of parameters and the speed of inference, we have counted the number of parameters of different methods and calculated the average inference time required for each image. It can be seen from Table 7 that the design of multitask learning and CAS-FB will increase the number of parameters and inference time, thereby increasing the space and time complexity.