## 2 Related Work  
Image segmentation is a prevalent task in computer vision applications. In recent years the advancement in deep learning algorithms and libraries led to a significant adoption of deep learning in computer vision tasks, including classification and segmentation. Prominent deep learning architectures for segmentation includes U-Net [6] and Mask R-CNN [7]. Algorithms were developed to localize and detect the optic disc with methods such as interval type-II fuzzy multi-level thresholding [8]. Another classical image processing approach was proposed by [PERSON] and [PERSON] [9]; their approach consists of locating the optic disc using PCA, blood vessels segmentation and inpainting, optic disc segmentation using extended local binary fitting (LBF) active contour, and cup segmentation using spatially weighted fuzzy c-means (SWFCM) clustering. This approach is fascinating but very involved, unlike the deep learning end to end approach. The approach was trained and tested on 59 images only, which does not give enough information about the robustness of the approach. [PERSON] et al. [10] used a convolutional neural network for localization by dividing the image into 13 \(\times\) 13 regions and framing the problem as regression to find the bounding box then use non-maximum suppression on the 13\(\times\)13 grid prediction for the final bounding box. For segmentation, [PERSON] et al. [11] used modified U-Net with short and long skip connections inspired by ResNet [12] to segment optic disk and exudate but not the optic cup. [PERSON] et al. [13] used a VGG16 style fully convolutional neural network (FCN) pre-trained on ImageNet to segment the optic disc and cup in Drishti-GS dataset [14]. The ONH in these images is centered in the image; hence they only center cropped the images to preserve the image resolution. [PERSON] et al. [15] released the REFUGE dataset along with the REFUGE challenge as an attempt to have a standardize benchmarking strategy for comparing different models for both segmentation and classification tasks. The training set has a different resolution and was acquired with a camera that is different from the validation and test sets (details on this are provided in the materials section). The competition was conducted in two phases and only 12 teams made it to the final stage, and their results were reported in the paper [15]. Each team used a different approach to tackle the segmentation problem. Most teams used a two-stage approach for segmentation which proved to be  
Figure 1: (a) A fundus image with (b) ophthalmologist annotation and (c) the generated ground truth mask for the optic disc and cup used in training and testing. successful in many cases [16]. AIML team used a ResNet-50 based FCN to locate the ONH then crop around it to one-quarter of the original size; the cropped images are then passed to four more ResNet networks ResNet-50, -101, -151, and -38 to segment the OD/OC. They used the REFUGE training set with augmentation to train the network. Test time augmentation is used in the four networks, and the final result is averaged across all individual outputs. BUCT team used two U-Nets [6] one for OD segmentation and the other for OC segmentation. First, they cropped the image to a square of size \(817\times 817\) keeping the ONH in the left side of the image, as the OHN in REFUGE dataset is located at an almost fixed general area within the fundus image. The cropped image is then resized and used for OD detection by the first U-Net, and ellipse fitting on the largest connected component was considered the OD. For the OC, the bounding box of the detected OD is used to crop the image with 100 pixels padding. The cropped image is fed to the second U-Net and the largest connected component is taken as the OC. Both U-Nets used REFUGE training set with data augmentation for the training process. CUHKMED team also used a two-stage approach, first localizing the OD using U-Net then using DeepLabv3+ [17] for segmentation.