# Extracting Retinal Anatomy and Pathological Structure Using Multiscale Segmentation
###### Abstract
First, the image database is a limitation that renders label indexing inaccurate. Second, retinal images with large-area pathological features or lesions interfere with normal regions, which seriously affects the segmentation effect. Finally, the image quality of a small number of retinas is affected by the environment at the time of collection, resulting in low feature detail and background contrast. Manual labeling of datasets depends on an operator's technical experience, which means that the number of subjective factors is large, thus rendering the method inefficient [19]. By comparing various networks, we find that the PixelNet [20] model (based on the number of pixel points of a feature map) is the most effective segmentation finder; this is suitable for retinal fundus image segmentation. The model uses a VGG-16 convolution to extract convolution features and extracts the corresponding features from multiple convolutional feature maps for a single sampled pixel. This creates a hyper-column descriptor that can be inputted as a feature of an MLP multilayer perceptron. Finally, the output of the classification result is determined. In addition, the main idea of the network lies in the sampling strategy used during training, which can accelerate the training step. Pixel-based stratified sampling or sparse sampling is often a problem. However, even when we sample from very few pixels of a training image, we can nevertheless get good results. This work also describes how to assemble an image database to provide a broader training set. Then, by using the PixelNet network structure, we replaced the original VGG-16 with VGG-19 to increase the width of the network. We also adjusted the output of each layer of the network structure to be consistent. Therefore, we can adjust the number of feature maps generated by each layer. We also notice that we can maximize the size of the compression model to improve the test rate under the condition that model accuracy is guaranteed. Therefore, the multiscale segmentation framework is achieved using our two contributions:  
1. A task-specific network that is designed to compute the retinal anatomy and pathological structure of a fundus. We collected a large-scale retinal dataset composed of 1500 images from 282 proliferative diabetic retinopathy (PDR) and non-proliferative diabetic retinopathy (NPDR)patients. PDR and NPDR are indicators for judging diabetic retinopathy grades. The difference between PDR and NPDR lies in blood vessel formation. We also know that PDR develops after NPDR. Therefore, we design an adaptive thresholding method to segment the FOV boundary from the background of the retinal fundus image. The design of the network can often support the boundary FOV segmentation and the automated segmentation training system, which is consistent with clinical requirements. 2. A multiscale network is proposed to achieve the final segmentation step, e.g., a four-input branch that enhances the integrity of segmentation and the ability to generalize the training model. The purpose does not neglect the integrity of the structure while training. The network is also error-tolerable, which means that the diagnostic accuracy can be guaranteed even when one of the branches is not accurate enough. Therefore, the innovation of our method lies in the four input branches. The network structure is not limited to segmentation of single retinopathy features. Our method is suitable for exudates,roth spots, blood vessels, and optic discs segmentation, and our database has an adaptive thresholding method to segment the field of view (FOV) boundary from the background of the retinal fundus image. In addition, we change the number of layers according to the complexity of the different features of the fundus to improve the method's efficiency. Finally, the network models are distributed to multiple GPUs for segmentation and validation, based on the test image set, 0 to achieve independent segmentation between the various structures in the images.