# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
###### Abstract  
Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods. retinal fundus images; vessel segmentation; centerline extraction; multitask learning  
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traditional machine learning methods and some deep learning methods. For example, [PERSON] et al. [14] used support vector machines for vessel segmentation, and [PERSON] et al. [15] designed an integrated system based on decision tree to achieve the segmentation of blood vessels from retinal images. Among the many machine learning methods, deep learning is currently the state-of-the-art method [16]. [PERSON] et al. [17] proposed a U-Net architecture that requires only a few annotated images to achieve high-precision blood vessel segmentation. [PERSON] et al. [18] presented a recurrent residual convolutional neural network named R2U-Net based on U-Net models and residual network models. [PERSON] et al. [19] proposed a network named Spatial Attention U-Net by introducing a spatial attention module that inferred the attention map along the spatial dimension and multiplied the attention map by the input feature map for adaptive feature refinement. [PERSON] et al. [20] designed a segmentation network, CS\({}^{2}\)-Net, which added an attention module in the encoder layer and decoder layer to segment the tubular structure. These methods are better than classical methods. There are also some state-of-art methods, such as generative adversarial networks (GAN), transfer learning, multitask learning, and so on. GAN has superior data classification capabilities [21]. [PERSON] et al. [22] used a conditional generation confrontation network to achieve accurate segmentation of fundus blood vessels. Transfer learning is a neural architecture trained in other fields first, then fine-tuned and applied to new tasks [23]. [PERSON] et al. [24] used the transfer learning method to pre-train the full convolutional network to realize the segmentation of multiple fundus datasets. The method based on multitask learning can associate different tasks, realize the sharing and mutual complement of features, and realize the improvement of performance between tasks. [PERSON] et al. [25] proposed a multitask learning method that simultaneously performs two tasks of blood vessel segmentation and arteriovenous classification, which enhances the performance of arteriovenous classification. [PERSON] et al. [26] designed a network framework to perform the task of extracting blood vessels and centerlines at the same time. Vessel segmentation is closely related to centerline extraction [27]. However, many studies have divided these two tasks into independent tasks, which may lead to discontinuities in the small part when only the blood vessel segmentation task is performed, and the single centerline extraction will cause a higher false-positive rate because of the lack of constraint of the segmentation result.