# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 2 Related Works  
### Retinal Layers Segmentation  
With the increased availability of OCT imaging devices, the detailed analysis of retina pathologies became possible. However, manual segmentation and measurement of tissue biomarkers are very time-consuming, and with an increased number of pathological subjects, not always an option. Thus, multiple image informatics algorithms have been developed in recent years to support the effort of ophthalmologists.  
Development of the algorithms for retina layers segmentation started over 20 years ago with classical image informatics approaches, that can be divided into: edge detection methods [17; 18; 19; 20], active contour modeling [21; 22; 23], and graph search and dynamic programming [24; 25; 26].  
As can be derived from analysis of the literature, the complexity of proposed methods advanced with years, as well as their accuracy of retina layers segmentation and number of layers that could be segmented. Since 2012 the graph-search methods proved one of the most accurate retina layers segmentation for healthy and pathological cases. Their disadvantage, however, is the need for extensive image preprocessing (primarily noise suppression) [27; 28] and careful selection of parameters for each dataset to make the designed approach suitable for the task. Additionally, the complexity and high time consumption make them inadequate for real-time application in a clinical setting.  
The progress has also been made with machine learning, pattern recognition, kernel, and clustering techniques [29; 30; 31]. Furthermore, after the expansion of convolutional neural networks (CNNs) in the field of image segmentation, fully convolutional networks (FCNs) became a useful tool for segmentation of biomedical images [32; 33; 34]. In 2017, the first attempt was made to use a network called ReLayNet [35] (a variation of UNet [33] and DeconvNet [36]) for retina layers segmentation.  
Table 1 lists a summary of neural network topologies utilized in current pixel-wise approaches for retina layers segmentation (excluding papers that combine pixel-classification with graph search technique). In addition, the literature was analyzed in terms of the number and size of images utilized for training and experiment settings (e.g., segmented layers, loss function, data augmentation methods). Any previously undefined diseases abbreviations are described at the end of the paper.  
The reviewed works focus on segmenting main retina layers in normal and pathological subjects and, in some cases, accompanied fluids. Although the available databases consist of a limited number of training images, researchers compensate for that with data augmentation methods. The majority of published methods are based on UNet architecture [33], its modifications (with dilated convolutions [40], batch normalization [35], dropout layers [42]) or combination with other networks, such as ResNet [45]. Aside from accuracy, the advantage of the facilitation of neural networks for retina segmentation is their ability to simultaneously segment multiple layers as well as fluids and pathological tissues. Furthermore, contrary to classical solutions, they do not require a separate set of models and parameters for each specific disease or normal case.