## 2 Related Work
The training loss is calculated using the vessel segmentation loss and the final segmentation loss. The generated masks are then post-processed by filling the holes and generating convex hulls from the results. Finally, WinterFell team used Faster R-CNN first to detect and crop the ONH then a ResUnet for OD/OC segmentation. They used two preprocessing steps, first normalizing all images using a reference image then inverting the green channel from the RGB color space. A common problem with medical image analysis is the limited amount of data which can be tackled by synthesizing new data using, for example, conditional generative adversarial networks [20], using extreme augmentation [21], or leveraging transfer learning [22]. We use the transfer learning technique in this paper. [PERSON] and [PERSON] in their review article [23] gave an overview of six convolutional neural network architectures that are usually used in transfer learning and they listed their applications in diabetic retinopathy classification in the literature. For this paper, we used the REFUGE dataset with its train/test split setting, and we added Magrabi male, Magrabi female, and MESIDOR datasets from [24] as additional test sets to check the robustness of the proposed solution. The ONH inside Magrabi and MESIDOR fundus images is not located in a fixed position, like for example the center of the images (Drishti-GS), or in the center-left side of the image (REFUGE), images from Magrabi and MESIDOR data sets have the ONH in different areas of the image. Hence, we cannot merely center crop the images or crop the image around the center-left as some teams did in the REFUGE challenge. Our approach includes first localizing the ONH from the image and cropping around it, then using the cropped portion of the image for segmentation. For both tasks, Mask R-CNN is used, and unlike most of the teams in the REFUGE competition, we will not use an ensemble of network or augmentation for segmentation. The following are this paper's main contributions:  
* Simpler and less computationally expensive method compared to state-of-the-art for retinal fundus image segmentation. The top-performing team in the REFUGE challenge utilized data other than the training set and used an ensemble of many Mask-RCNNs. Our method with a hierarchal approach with only two Mask-RCNNs and a training trick by modifying the loss weight for the second stage in the training reached the level of the top-performing team. The model trained well with this method transfer to other datasets without being retrained. * A new training strategy for fine-tuning a network for improved detection of optic nerve head from fundus images. This includes changing the region proposal network detection scales and using the cropped images from the first stage alongside the original images for the training. The remainder of this paper consists of four sections: (1) Materials and Methods: contains a description of the architecture, details about the methodology, and the datasets used. (2) Results: present the experimental results of the proposed method on the datasets described in the previous section. (3) Discussion and conclusion: a short discussion about the results, final remarks, and possible future work.