# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 3 Materials  
### OCT Image Dataset  
The goal of conducting research in OCT retina layers segmentation creates a need to obtain numerous images with annotated biomarkers searched for in a specific task. Since 2018 only several public OCT databases with 3D images have been established, and most of them are aimed at classifying a specific disease from a single B-scan. Other cohorts, focused on automatic retina layer segmentation, provide manual segmentations for 3 to 8 retina borders. Most contain images of 10 to 25 volumes [37; 54; 55; 56], although one database consists of over 380 scans [57]. The subjects included in those databases are either healthy or involve patients suffering from pathologies such as age-related macular degeneration (AMD), diabetic macular edema (DME), diabetic retinopathy (DR). Nevertheless, none of the available databases concern patients with vitreoretinal interface pathologies, especially vitreomacular traction, not to mention annotations of PCV.  
Thus, a CAVRI (Computer Analysis of VitreoRetinal Interface) dataset [16] of OCT images with VMA and VMT has been created to analyze the characteristics of preretinal space. Subjects for this dataset were recruited at the Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences in Poznan, Poland. The study was approved by the Bioethics Committee of Poznan University of Medical Sciences under resolution no. 422/14. All participants signed an informed consent document before enrollment.  
The CAVRI database contains 3D images of the macula obtained with the Avanti RTvue device (Optovue, Incorporated, Fremont, CA, USA) using the 3D Retina scanning protocol. For this research, from a group of 73 cases a set of 50 OCT volumes was selected: 25 examples of the healthy retina (with asymptomatic vitreomacular adhesion (VMA)) and 25 subjects with VMT. Each 3D scan consists of 141 cross-sections with 640 \(\times\) 385 px resolution representing 2 \(\times\) 7 \(\times\) 7 mm retina volume. The corresponding voxel sizes equal 3.125 \(\upmu\)m in the vertical direction (further denoted as \(y\)), and 18.18 \(\upmu\)m and 49.65 \(\upmu\)m in fast-scanning (\(x\)) and non-fast scanning (\(z\)) directions, respectively. No multi-sampling and noise-reduction protocols were used during acquisition or preprocessing. All 7050 cross-sections (also called B-scans), visualized as gray-scale images, were analyzed separately.  
The PCV line, visible as a hyperreflective line in a B-scan (see Figure 1), was manually annotated under the supervision of clinical experts (three ophthalmologists) using a custom-made public software OCTAnnotate [58]. Selected 50 subjects had a maximum difference of manual segmentation between the experts less than 3 px. In addition, two other lines denoting retina borders were also labeled for investigational purposes, namely the inner limiting membrane (ILM) and retina pigment epithelium (RPE) outer border. These three lines (and consequently four image regions they create) are the ground truth for this research. Based on reference segmentation of PCV and ILM lines, we calculated the preretinal space volumes as the main metric of comparison. The average value of preretinal space volume for VMA is \(3.17\pm 1.96\) mm\({}^{3}\) and for VMT is \(12.19\pm 6.09\) mm\({}^{3}\). The Wicocontest resulted in \(p\)-value close to zero (\(3.6\times 10^{-10}\)), which confirms significantly different preretinal space volumes for the VMA and VMT groups.