## 2 Related Work  
In the past, many traditional algorithms can be used for medical image segmentation, including threshold-based segmentation [5], region-based image segmentation [6], edge detection-based segmentation [7], wavelet analysis and wavelet transform image segmentation method [8], genetic algorithm-based image segmentation [9; 10], active contour model-based segmentation method [11]. However, traditional segmentation methods have some problems, such as the need for human intervention in the segmentation process, prior knowledge before segmentation, the existence of over-segmentation in segmentation, and low segmentation accuracy. Deep learning-based methods for image segmentation have made significant achievements. Their accuracy has surpassed that of traditional methods. [PERSON] et al. [12] proposed a fully convolutional network for semantic segmentation to classify images at the pixel level, solving the problem of semantic level image segmentation method. Based on it, [PERSON] et al. [13] proposed a 3 DFCN network structure. They optimized the spine structure segmented by the 3D FCN network using a deformation model algorithm, which further improved the accuracy of the segmentation of spinal MR images. [PERSON] et al. [14] combined the FCN algorithm and the majority voting algorithm to segment 19 targets in the human torso CT image. However, the results obtained by FCN are still not subtle enough. It classifies each pixel, does not adequately consider the relationship between pixels, and is not sensitive to the details in the image, so it is not suitable for the segmentation of blood vessels. U-Net [3] is undoubtedly one of the most successful medical image segmentation methods. After the emergence of U-net, many network variants appeared. For example, [PERSON] et al. [15] proposed a recursive residual convolutional neural network (R2U-net) based on U-net. This residual network can achieve deeper Network, R2U-net can make the network extract more features than U-Net. LadderNet [16], proposed by [PERSON], uses an improved residual block, where two convolutional layers in one block share the same weight, and LadderNet has more information flow paths than U-Net. This allows it to get more accurate segmentation results. Similar to Ladder, [PERSON] et al. [17] proposed a dynamic deep network, which adds a small U-net to classify the blurred areas of the image in a more refined manner, thereby achieving higher accuracy. Currently, the network with the most top performance on the DRIVE data set is IterNet [18] proposed by [PERSON] et al. Its structure is very similar to the dynamic deep network, has a u-net, and a mini u-net. And the blurred details of the blood vessel to be divided can be found from the segmented image itself. It has obtained the highest AUC value of 0.9816 on the DRIVE data set [4]. However, these networks do not fully consider the correlation of each channel, and a lot of effective information will be lost during the training process, so this paper introduces a channel attention mechanism. In many image segmentation tasks, the attention module is introduced. For example, SeNet proposed by [PERSON] et al. [19], it automatically learns the importance of each feature channel through learning, then according to the importance to enhance useful features and suppress useless for the current task. [PERSON] et al. [20] proposed the EncNet structure, in which the Context Encoding Module is used to capture the semantic context of the scene and selectively emphasize the category-related feature maps. [PERSON] et al. [21] proposed the Feature Pyramid Attention module (FPA) and Global Attention Up-sample module (GAU) and introduced an attention mechanism for semantic segmentation. [PERSON] et al. [22] proposed DANet to capture frame feature dependencies in space and channel dimensions. The position attention module can learn the correlation of spatial features, and the channel attention module can model the correlation of channels.