# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
###### Abstract  
This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optvue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.  
human eye image analysis; preretinal space; retinal layer segmentation; convolutional neural networks; UNet; optical coherence tomography +
Footnote †: journal: Pattern Recognition  
0000-0001-5000-0000]G. A. A.  
For example, Figure 1 illustrates OCT images of a healthy retina with a visible attachment of PCV and a pathological case of vitreomacular traction.  
In VMT, the posterior vitreous with continuous adhesion to the macula exerts traction on the retina (mainly fovea--the most sensitive part of the macula). Persistent traction can lead to deformation of the fovea, cystoid foveal thickening, and disorganization of retinal layers. Such problems manifest with metamorphosis (image deformity), deterioration of visual acuity, blurred or impaired central vision that significantly impair daily tasks (e.g., reading) [11].  
It has also been reported that the prevalence of VMT increases significantly with age, from 1% in subjects of 63-74 years old to 5.6% in patients over 85 years old [12]. The chance of spontaneous VMT resolution is up to 23% [13]. However, if left untreated, the probability of severe retina damage (frequently due to the development of a macular hole) and vision deterioration increases with time. Systematic monitoring of preretinal changes allows the physician to determine if (and when) surgical intervention is required.  
Currently, VMT advancement is determined based only on a single cross-section through the center of the macula. To the best of our knowledge, no research or commercial image informatics solutions allow for automatic segmentation of the vitreous cortex and thus the preretinal space. Therefore, manual measurements can only be made in a few points of the volume and are not sufficient to quantify the profile of this epiretinal pathology. A numerical analysis of preretinal space volume and adhesion area is required to assess precisely the development and current stage of VMT. Such investigation can include: quantification of the preretinal space volume [14], statistical analysis of vitreo-retinal interface parameters (e.g., adhesion area), description of the stage of pathology development [15] and its changes in time. Therefore, the eye doctor community highly desires fully automated OCT image analysis presented in this paper as a necessary step in advancing the fundamental understanding of the changes in vitreoretinal interface pathologies. Automated segmentation of preretinal space and thus 3D volumetric analysis would optimize available treatment strategies, including ocriplasmin, sulfur hexafluoride, and octafluoropropane gas injection [14].