# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 2 Related Works
### Preretinal Space Segmentation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Network** & **Task** & **Dataset** & **Loss Function** & **Data Augmentation** \\ \hline ReLayNet [35] & 7 layers and fluid & \begin{tabular}{l} Duke SD-OCT public DME dataset [37] \\ 11B-scans each (512 \(\times\) 740 px), \\ 110 images in total \\ \end{tabular} & \
\begin{tabular}{l} Weighted Dice and Cross Entropy Loss \\ \end{tabular} &
\begin{tabular}{l} horizontal flip, spatial \\ translation, cropping \\ \end{tabular} \\ \hline
3D ReLayNet [38] & 7 layers & \begin{tabular}{l} 13 volumes (13 normal subjects), \\ 10 B-scans each, \\ 130 images in total \\ \end{tabular} & \begin{tabular}{l} Cross Entropy Loss \\ \end{tabular} & none \\ \hline FCN8 [39] & 4 layers & \begin{tabular}{l} 10 volumes (5 patients with CSC, 5 normal eyes), \\ 128 B-scans each (512 \(\times\) 1024 px), \\ 128 images in total \\ \end{tabular} & \begin{tabular}{l} Weighted Cross Entropy Loss \\ \end{tabular} & none \\ \hline LFUNet [40] & 5 layers and fluid & \begin{tabular}{l} 58 volumes (25 diabetic patients, 33 healthy subjects), \\ 245 B-scans each (245 \(\times\) 245 px), \\ 14210 images in total \\ \end{tabular} & \begin{tabular}{l} Weighted Dice and Cross Entropy Loss \\ \end{tabular} & \begin{tabular}{l} horizontal flip, \\ rotation, scaling \\ \end{tabular} \\ \hline DRUNet [41] & 6 regions & \begin{tabular}{l} 100 scans (40 healthy, 41 POAG, \\ 19 PACG), \\ single B-scan through ONH each (468 px width), \\ 100 images in total \\ \end{tabular} & Jaccard Loss & \begin{tabular}{l} horizontal flip, rotation, \\ intensity shifts, white \\ noise, speckle noise, \\ elastic deformation, \\ occluding patches \\ \end{tabular} \\ \hline Uncertainty UNet (U2-Net) [42] & \begin{tabular}{l} photoreceptor layer \\ \end{tabular} & \begin{tabular}{l} 50 volumes (50 patients: 16 DME, 24 RVO, \\ 10 AMD+CNV), \\ 49 B-scans each (512 \(\times\) 496 px), \\ 2450 images in total \\ \end{tabular} & Cross Entropy Loss & none \\ \hline UNet with pretrained ResNet weights [43] & 4 layers & \
\begin{tabular}{l} 23 volumes (23 AMD patients), \\ 128 B-scans each (1024 \(\times\) 512 px), \\ 1270 images in total \\ \end{tabular} & Weighted Log Loss &
\begin{tabular}{l} horizontal flip, \\ rotation \\ \end{tabular} \\ \hline
2 cascaded UNets & \begin{tabular}{l} 35 volumes (53 patients: 21 with macula sclerosis, \\ 14 healthy), \\ 49 B-scans each (496 \(\times\) 1024 px), \\ 1715 images in total \\ \end{tabular} & \
\begin{tabular}{l} 1 st: Dice Loss, \\ 2 nd: MSE Loss \\ \end{tabular} &
\begin{tabular}{l} horizontal flip, vertical \\ scaling \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of neural networks used for pixel-wise semantic segmentation of retina layers in OCT images. is visible through the entire width of the image, which, due to its varying density, is not necessarily true. Furthermore, automated segmentation was used for quantitative analysis of epiretinal abnormalities only in several studies [48; 49; 50] (for epiretinal membrane and macular hole). The lack of research in PCV segmentation is caused by the unavailability of the data (OCT scans and manual segmentation) with both VMA and VMT, and insufficient accuracy of state-of-the-art retina segmentation methods when applied to this task.