## 1 Introduction  
The macular region is located in the center of the retina and is the most critical and sensitive part where more than 90% of the cones are concentrated, being responsible for determining the light, shape, and color vision of the human. Macular edema occurs in the macular region, and it is one of the three major retinal diseases. It is mainly caused by the rupture of vascular epithelial cells and mural cells between the blood vessels and the retina, causing damage to the retinal fluid transport cells, which can cause unclear vision and vision loss, deformation of sight, and even blindness in severe cases. Spectral domain optical coherence tomography (SD-OCT) is a non-invasive imaging mode that is typically used to achieve a high-resolution (6 \(\upmu\)m) cross-section scan of biological tissue with a depth of penetration (0.5-2 mm) [1]. Figure 0(a) shows the normal retina optical coherence tomography (OCT) image, Figure 0(b) shows the OCT image of macular edema, and Figure 0(c) shows red regions in the image that are annotations of edema lesions. For the past two decades, optical coherence tomography has been recognized as one of the most important instruments in ophthalmic diagnosis. Ophthalmologists can screen and diagnose ocular patients by the number and size of cysts in the macular region. However, the segmentation of macular edema in OCT images has always been a challenge for clinicians because of the high acquisition rate of the OCT system, which generates a large amount of 3D data, and each 3D volume contains hundreds of 2D B-scan images [2]. It requires a long processingtime to manually annotate each B-scan image and is unrealistic for clinical applications. In addition to the above difficulties, both the image noise and severe retinopathy can lead to the boundary between healthy tissue and diseased tissue to spread highly [3]. This diffusion may lead to inconsistent manual annotation results among the different experts, and manual segmentation of edema regions will become very subjective. Therefore, in the past 10 years, many scholars have conducted important research on segmentation and detection of macular edema in retinal OCT images, obtaining many remarkable semi-automatic and automatic segmentation methods. There are two main categories: (1) those based on traditional methods, and (2) those based on deep learning. For the traditional method, in 2005, [PERSON] used a deformable model to delineate filled regions in OCT images of patients with the age related macular degeneration [4]. In 2010, [PERSON] and [PERSON] et al. reported a three-dimensional analysis method of retinal layer texture, which can identify fluid-filled regions in SD-OCT of the macula [5]. In 2013, [PERSON] et al. proposed a four-step process of first segmenting all low-reflection regions in an image as candidate regions, and then performed pre-processing, coarse segmentation, fine segmentation, and quantitative analysis on the candidate regions to automatically segment the intraretinal fluid (IRF) and the subretinal fluid (SRF) [6]. In 2015, [PERSON] et al. well used a layer-dependent stratified sampling strategy to segment the intraretinal and subretinal fluid in OCT images [7]. [PERSON] et al. modified the dynamic programming-based approach to classify the layers and segment fluid using the kernel regression-based approach, and then fine-estimated them using the graph-based dynamic programming approach in the second phase [2]. Following a similar path, in 2016, on the basis of graph segmentation technology, [PERSON] et al. proposed a structured random forest method to learn layer-specific edges and to enhance segmentation [8]. In the same year, [PERSON] et al. modified a method on the basis of dynamic programming by considering the spatial dependence of a frame and its two adjacent frames [9]. This has made some great contributions to the segmentation of macular edema in retinal OCT images.