# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Multitask Learning Network  
The proposed multitask learning network, MSC-Net, performs the two tasks of vessel segmentation and centerline extraction. The segmentation task can focus on the overall shape and edge information of the blood vessel. However, the segmentation result usually loses small features because of the imbalance of the scale of the blood vessel. The centerline has uniform voxel points at each scale of the blood vessel; therefore, the centerline extraction branch can avoid the imbalance of the scale and pay more attention to the continuity and topological characteristics of the blood vessel. The overall shape and edge information can constrain the centerline and reduce the influence of noise on the centerline extraction result. The information of continuity and topological structure can repair broken blood vessels and lengthen small blood vessels. Therefore, the information of the two branches in the MSC-Net can be merged and mutually restricted to improve the results of segmentation and centerline extraction. As shown in Figure 2, the proposed multitask learning network, MSC-Net, consists of two branches for vascular segmentation and centerline extraction, respectively, and a feature fusion path. The two branches perform feature extraction and output the prediction results of blood vessel segments and centerline. Similar to U-Net [17], segmentation and centerline extraction branch have a down-sampling path, an up-sampling path, and skip connections. A fusion path is added between the down-sampling paths of the two branches to fuse the features and achieve the mutual constraints of the two tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Quantity** & **Resolution** & **Train/Test** &
\begin{tabular}{c} **Cross** \\ **Validation** \\ \end{tabular} \\ \hline DRIVE & 40 & \(565\times 584\) & 20/20 & No \\ STARE & 20 & \(700\times 605\) & 15/5 & Yes \\ CHASE & 28 & \(999\times 960\) & 21/7 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of the three public fundus datasets used: total number of images, image resolution (width \(\times\) height), training and testing split, and whether to perform cross-validation. Figure 2: The network architecture of MSC-Net consists of a blood vessel segmentation branch, a centerline extraction branch, and a fusion path. The fusion path aggregates the features of the two branches to effectively correct and supplement the features of the two branches. Take the segmentation branch as an example from Figure 2, and the input \(I\) is the retinal image after data processing. First, a feature map is obtained through a \(1\times 1\) 2D convolution and then input into the down-sampling path. The down-sampling path is composed of four encoders for feature extraction. Each encoder includes a down-sampling layer and two \(3\times 3\) layers of convolution, BatchNorm, and rectified linear activation unit (Conv+BN+ReLU). Considering the low-level and different scale image features, MSC-Net uses skip connections to input the features after the first Conv+BN+ReLU layer of each encoder into the upper-level up-sampling path. The input of each encoder is the output of the previous module. In the up-sampling path of the segmentation branch, feature restoration and up-sampling are carried out through four decoders. A decode block is composed of two groups of \(3\times 3\) Conv+BN+ReLU layers and one up-sampling layer. In each decoder, skip connection is used to concatenate the feature map output by the first Conv+BN+ReLU layer, and the features obtained from the down-sampling path are concatenated and input to the next Conv+BN+ReLU layer. The input of each decoder is the output from CAS-FB (see Section 2.3 for details). After passing the up-sampling path, the feature map size is restored to the same size as the original image. Finally, a \(1\times 1\) convolutional layer and a sigmoid activation layer are used to predict the probability of the vessel segmentation result \(S^{\prime}\).