# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 2 Related Works
### Issues with Layers Topology  
As was described above, the convolution neural networks typically output probability maps classifying each pixel belonging to one of the designed classes. However, it means that individual pixels are analyzed locally, while this methodology gives results that may perfectly match regional data, if we look closely at a B-scan (see Figure 1), it can be noticed that many areas belonging to different layers or image regions have similar intensities or contrast characteristics. As a result, CNNs produce inconsistencies in the topological order of retina layers that are unacceptable in medical imaging [51].  
One of the first approaches to address that issue was proposed by [PERSON] et al. [44; 51]. In their works, two separate networks were used: first to learn the intensity features and second to correct the obtained topology by learning the implicit latent network features corresponding to shape factors in an adversarial manner. Some other works on topology-guaranteed predictions tried to directly predict the coordinates of layer boundary while encoding prediction of lower layers as a relative position to the upper ones [52]. Nevertheless, such an approach may easily lead to error propagation if the uppermost boundary is incorrectly segmented.  
A promising approach was proposed by [PERSON] et al. [53], in which they integrated information about the hierarchical structure of the image in the form of a relative distance map (RDM). This map, computed from an initial graph search-based retina borders segmentation in a preprocessing step, was provided to the neural network as a second channel of the input image. This work was further extended by [PERSON] et al. [40] by substituting the graph search-based initial segmentation with cascading networks trained separately.  
The relative distance map provides a way of introducing additional spatial information alongside the input image. Each pixel value of the map corresponds to the pixel position in the image as a function of retina location. Thus, knowing the coordinates of inner and outer retina borders (namely ILM and RPE) across a B-scan, the intensity values of the relative distance map are computed for each pixel with indexes \((x,y)\) as follows:  
\[M(x,y)=\frac{y-ILM(x)}{RPE(x)-ILM(x)}\, \tag{1}\]  
where \(ILM(x)\) and \(RPE(x)\) represent the \(y\) (vertical) coordinate of previously segmented ILM and RPE lines in the image column \(x\). According to this equation, the pixels above the ILM take value \(<0\), pixels positioned within the retina tissue take values in the range of \(\langle 0,1\rangle\), and pixels below the retina are \(>1\). Such weighing scheme, concatenated (as a second channel) to an original OCT image, which is also in the range of \(\langle 0,1\rangle\), allows the network to learn layers topological dependence. Such procedure boost precision of segmenting non-neighbouring layers with similar intensity patterns or lower contrast.  
Our work explores the possibility of utilizing deep neural networks (DNN) for the task of preretinal space segmentation and investigates the challenges connected with the specificity of the preretinal space (shape and image intensity variations). We present the research results for preserving the topological correctness of the segmented OCT image areas, including the implementation of four different distance maps (based on prior segmentation or without it). We propose investigating the influence of convolutional kernel size and shape on topology correctness and overall segmentation accuracy.