# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
## 2 Related Work  
The traditional methods used to segment the optic disk and optic cup are mainly based on manual feature extraction. [PERSON] et al. and [PERSON] et al. [17; 18] used a circular transform-based method to segment OD. [PERSON] et al. [19] used super pixel point classification to segment OD and OC, which transformed the problem of locating the OC boundary to a pixel-by-pixel classification problem. [PERSON] et al. [20] attempted to segment the optic disk using a weakly supervised approach. Compared with optic disk segmentation, the low contrast ratio causes optic cup segmentation to be more difficult. The segmentation accuracy of traditional segment methods is determined by the image quality and the depth of the manually extracted image features, and when effective features cannot be extracted, the segmentation accuracy will be significantly reduced. With the continuous development and popularity of deep-learning technology, [PERSON] et al. [21] discovered that the application of convolutional neural networks (CNNs) to extract image features achieved better results than traditional algorithms on various medical image segmentation tasks. FCN is the basis of many current semantic segmentation methods and has achieved better segmentation results than traditional methods on natural image segmentation tasks. Therefore, deep-learning techniques have been introduced into medical image processing, and more and more studies have been conducted using convolutional neural networks. OD and OC segmentation networks based on deep learning have been proposed one after another and achieved better segmentation results than traditional segmentation methods. M-Net (multi-label deep network) added multi-scales based on U-Net, introduced the idea of depth supervision, and added additional loss functions in the middle interlayer with an additional loss function. The introduction of the polar coordinate transformation operation successfully realizes the simultaneous segmentation of the OD and OC. [PERSON] et al. [22] proposed DenseNet which applied the FCN to the OD and OC segmentation tasks. However, most existing CNN-based methods cannot obtain detailed boundary features due to multiple pooling and downsampling, resulting in the ambiguity of segmentation boundaries. [PERSON] et al. [23] proposed CENet (context encoder network), which realized a context-encoding module consisting of a residual multi-path pooling module and a multi-scale dense dilated convolution module. CENet can capture features with high-level semantic information at multi-scales, but it is not used in the segmentation of OC. CDED-Net adopted a tightly connected optic cup-optic disk decoder network structure and achieves high performance in the joint segmentation of cups and disks, but extensive networks for segmentation tasks increase the number of required parameters. [PERSON] et al. [24] proposed a feature-embedding framework that effectively improves the generalization ability of convolutional neural networks in completing the OD and OC segmentation tasks. To summarize the above, these manners enhance the information extraction ability of the network by improving the structure, but the utilization of multi-level features in the middle of the network is not sufficient. DeepLabv3+ [15] used the convolution of upsampling filters to extract dense feature maps and capture a long-distance context, which is mainly divided into encoder and decoder parts. Xception is used as the backbone network in this paper. Then, the ASPP structure is used to solve the multi-scale problem. The decoder part is introduced to combine the underlying features with high-level feature fusion and obtain a notably high segmentation boundary accuracy. Despite these improvements in the segmentation network, DeepLabv3+ has more parameters, which may lead to long training times. Transformers in the field of computer vision have taken shape, and the Transformer architecture model has achieved significant results in the field of CV for its self-attention feature.