## 2 Related Work
They also used patch-based output space adversarial learning [18] to tackle the difference between the training and test/validation sets. The final segmentation is computed using an ensemble of five models. They used the training set from REFUGE to train the network and the training combined with validation set for adversarial learning. Cvblab team used a two-stage U-Net, the first one to detect and segment the OD, and the result of this is used to crop the image for OC segmentation. The OC segmentation is done using another U-Net specially trained to capture OC from the cropped image. Contrast Limited Adaptive Histogram Equalization was also used as a preprocessing step. Unlike the previously mentioned approaches, [PERSON] team used extra data for the training including, REFUGE training set, DRIONS-DB, DRISHTI-GS, and RIM-ONE v3. The Mammoth team used Mask R-CNN for OD localization and segmentation then Dense U-Net for segmentation. The Mask R-CNN output is used to crop the image around the detected OD, and the cropped image is used as input to the Dense U-Net. The Dense U-Net is used to get another segmentation of the OD. They used the probability values from the first OD segmentation output as an extra input layer for another U-Net to segment the OC. The output of both networks is ensembled to get the final prediction. The [PERSON] team also used Mask R-CNN at the first stage to localize the ONH and crop the image around it. In the second stage, the training set was divided into 14 subsets using the bagging technique. The images were preprocessed with different techniques. Each subset is trained with Mask R-CNN, U-Net, and Mnet for OD and OC segmentation and the final segmentation result is determined using voting among all the networks. NightOwl team used two dense U-Nets; the first gives a coarse localization of the ONH and then the image is cropped around the localized ONH. The other network performs the segmentation. Post and pre-processing steps were used to enhance the detection such as histogram matching and exponential transform in preprocessing as well as morphological operations and Gaussian smoothing in postprocessing. An augmented version of the REFUGE training set was used to train the network; augmentation was used to reduce the imbalance between glaucoma and non-glaucoma samples. NKSG team used DeepLabV3+ for the segmentation and used the ONH area as input to the network. They did not mention explicitly how the ONH area was selected, whether it was automatically found or manually cropped. Pixel quantization was also used as a preprocessing step. SDSAIRC team also used a cropped version of the image for the training of their multistage networks. The Mnet was used for both OD and OC segmentation. First, the predefined region of interest (ROI) of the fundus image was fed to the first Mnet to detect the OD then a bounding box around the detected OD was used to crop the image further and feed it to another Mnet for OC segmentation. The output was then post-processed using ellipse fitting. Histogram matching was used to transform the test and validation images to match the training image set. SMILEDeepDR used a different approach where a modified U-Net with squeeze and excitation was used for both glaucoma classification and for optic cup/disc segmentation. The inclusion of squeeze and excitation approach to U-Net was introduced by [PERSON] et al. [19] for prostate zonal segmentation on Magnetic Resonance (MR) images. The SMILEDeepDR team trained the network first to carry the classification task then a new network was fine-tuned to conduct the segmentation starting with the classification network weights. The segmentation was posed as an L1 loss rather than cross-entropy. VRT team used a modified U-Net with an auxiliary CNN branch that takes the vessel segmentation mask to generate estimated OC/OD masks. The output of this network is connected to the bottleneck layer of a second U-Net to obtain the final segmentation.