## 1 Introduction  
Optical coherence tomography (OCT) is a high-resolution three-dimensional imaging technique, which has significant advantages such as high speed, real time, and non-invasiveness [1]. OCT is widely used clinically and becomes the gold standard in diagnostic imaging for the leading macular diseases such as Diabetic Macular Edema (DME) and choroidal neovascularization(CNV) [2]. DME, caused by fluid leakage from damaged macular blood vessels, is the most common cause of vision loss in American adults [3]. Ophthalmologists use retinal thickness maps to assess the severity of DME. However, manual segmentation of the cyst area is a time-consuming task and prone to human error [4]. Thus, it is necessary to promote the automatic segmentation method of OCT image, which runs fast and prevents subjective factors. Since the 2000s, classic machine learning methods have been widely used for tasks related to the retina segmentation [5]. [PERSON] et al. [6] applied a kernel regression-based method to classify the retinal layer and subsequently used Graph Construction as well as dynamic programming to refine the process. [PERSON] et al. [7] utilized structured random forests to learn specific edges of the retinal layer to enhance Graph Construction. Additionally, he improved the spatial consistency between OCT frames by adding appropriate constraints to the Dynamic Programming paradigm for segmentation. [PERSON] et al. [8] proposed an auto-context loop for joint segmentation of retinal layer and fluid. For any segmentation target, deep learning has got comparable or better results to previous methods of classic machine learning [5]. The full convolutional neural network (FCN) [9] has achieved remarkable results in the field of image segmentation. [PERSON] et al. were inspired by the FCN network and proposed the U-Net [10], which combines deep semantic information and spatial information through encoder blocks, decoder blocks and skip connection. U-Net architectures, having achieved the best results in many medical image segmentation tasks, are widely used in OCT segmentation, such as optic nerve head tissues segmentation [11], drusen segmentation [12], intraretinal cystoid fluid (IRC) segmentation [13], fluid regions segmentation [14], and retinal layers segmentation [15]. [PERSON] et al. [16], achieving the best results in the RETOUCH competition, applied Graph-Cut to perform layer segmentation on OCT images as pre-processing and utilize U-Net to segment 3 types of fluid. [PERSON] et al. [13] used a cascaded U-Net network to segment the OCT fluid region and achieved very good accuracy. Inspired by U-Net, [PERSON] et al. [17] proposed ReLayNet which achieved accurate results in a joint segmentation of seven retina layers and fluid region in pathological OCT scans. A limitation of U-Net is that the consecutive pooling layers reduce spatial information of the feature to learn higher abstract feature representations [18]. Nevertheless, dense prediction tasks need rich spatial information. CE-Net [18], using the first few layers of the pre-trained resnet-34 [19] model based on the ImageNet data set [20] as the network encoder part and applying the decoder part they proposed, have achieved good results in OCT layer segmentation, cell boundary segmentation and lung segmentation. [PERSON] et al. [21] improved the U-Net and proposed a nested U-Net network (U-Net++) structure to take advantage of deep supervision [22] and re-designed skip pathways which aim at reducing the difference in semantic information between the encoder and decoder blocks. Additionally, a lot of mechanisms have been utilized to segmentation tasks. The multi-scale input, aiming at achieving multiple level sizes of receptive field, has been demonstrated to improve the performance of segmentation network [23; 24]. Side output mechanism, first proposed by [25], helps to resolve gradient vanishing problem and capture multi-level representations. Attention mechanism, widely applied in many tasks [26; 27; 28; 29], is able to capture long-range dependencies.