## 1 Introduction
With the improvement of graphics processing unit performance and the proposal of new deep neural network models, deep neural network has been widely used in medical image segmentation, as well as in other fields. In 2015, full convolutional neural network (FCN) [10] presented excellent performance in image segmentation tasks. In 2017, on the basis of FCN and fully connected conditional random field (CRF), [PERSON] et al. proposed a new method for segmentation of macular edema in OCT images [11]. The framework uses a convolution layer to extract and predict the pixel category of the input image, using fine-tuning to exploit the learning convolution kernel of the classification network to complete the segmentation task; then, according to the fully connected CRF model of image and FCN, the feature vector of heat map is outputted, including the pixel positions and values in different channels, and finally each pixel is classified, generating an amended prediction map as the final output. In 2015, U-Net came out, wherein its skip connection structure combines deep and shallow features, improves segmentation accuracy, and is widely used in the field of medical images [12]. The application effect is better than other frameworks in small datasets of medical image segmentation, such as neural structures, kidneys [13], and liver tumor [14]. In 2017, [PERSON] et al. proposed ReLayNet [15],  
Figure 1: Optical coherence tomography (OCT) images and the annotations of the edema regions: (a) normal retina OCT image; (b) OCT image with macular edema; (c) the annotations of the edema regions. which is suitable for OCT macular edema segmentation by the U-Net as a framework. The network segmented the edema region with OCT images and achieved good segmentation results. In 2018, [PERSON] et al. proposed a two-stage full convoluted neural network based on U-Net [16]--the first stage network was used to extract retinal regional features, and the second stage network was used to combine retinal information extracted in the previous stage for edema segmentation. In 2019, [PERSON] et al. improved the network parameters and loss function of U-Net in order to solve the imbalance problem if the categories in retinal OCT images, and added batch normalization (BN) between the convolution block and the activation function, which further improved the accuracy [17]. In 2020, on the basis of U-Net, [PERSON] et al. used multi-scale convolution instead of ordinary \(3\times 3\) convolution to achieve the adaptive field of the image. The channel attention module is embedded in the model, so that it can ignore irrelevant information and focus on key information in the channels. [18]. In 2020, [PERSON] et al. took advantage of the multi-scale input, multi-scale side output, and dual attention mechanism, presenting an enhanced nested U-Net architecture (MDAN-UNet) and achieving great performance with multi-layer segmentation and multi-fluid segmentation [19]. In same year, [PERSON] et al. used image enhancement and the improved 3D U-Net to achieve a fast and automated hyperreflective foci segmentation method, obtaining good segmentation results [20]. Recently, [PERSON] et al. integrated the segmentation of region of interest (RoI), proposing a HyCAD hybrid learning system for classification of diabetic macular edema, choroidal neovascularization, and drusen disorders [21]. At present, due to the loss of shallow features and the influence of image noise, the existing segmentation methods have not achieved a more ideal segmentation effect on the whole and diverse edema lesions. Therefore, this paper proposes a new method for automatic segmentation of macular edema regions in retinal OCT images on the basis of improved U-Net++. This method makes full use of U-Net++'s redesigned skip connections and dense convolutional blocks, reducing the semantic loss of feature maps in the encoder/decoder subnetwork.