# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 6 Results
### Effect of Data Augmentation  
Table 6 shows the effect of applying data augmentation with each of the employed CNN methods. Since the graph search-based approach is not a machine learning method, data augmentation is not applicable here. As can be expected, the addition of more varying images helps to train the network. This strategy boosts the segmentation outcome in all the methods. A detailed analysis revealed that rotation and random cropping are the two strategies that best improve the segmentation results. This supports the observation that the angle setting for each patient is an individual parameter that can change even between examinations. Figure 7 illustrates preretinal space Dice score distribution for each of the above-discussed improvements. From the box plots, it can be deduced that not only the average value has increased, but the overall performance was also improved. We performed a statistical test of significant differences among models (UNet, LFUNet, Attention UNet, and ReLayNet). The data used for the test is the same as those used to calculate the average values of Dice scores for Preretinal Space in Table 5. Based on the ANOVA test, the \(f\)-ratio value is 40.91. The \(p\)-value is \(<\)0.00001. The result is significant at \(p<0.05\). Standard UNet architecture and LFUNet provide the best probability maps, although UNet has slightly better performance in segmenting preretinal space, retinal area, and PCV border. The Attention UNet and ReLayNet performed poorly here, even if their scores are better when employing data augmentation. Figure 8 presents examples of the obtained segmentation masks. Examples shown in Figure 8 include a set of representative cases of VMT and VMA and illustrate a qualitative comparison of the obtained results. It includes two VMT cases (rows 1 and 2), two VMA cases with perifoveal vitreous detachment (examples 3 and 4), and one VMA case of slight detachment over a wide area (last row). In the presented images, it is visible that poor evaluation scores in Table 6 for ReLayNet and Attention UNet are the effect of the network's difficulty in discerning the areas with similar intensities (namely: vitreous, preretinal space, and the region below the retina). As an effect, patches of those classes appear in incorrect places in the prediction masks. Such \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Average Dice Score (SD)**} & \multicolumn{4}{c}{**MAE (SD) [px]**} \\ \cline{2-9}
**Model** & **Vitreous** & \
\begin{tabular}{c} **Preretinal** \\ **Space** \\ \end{tabular} & **Retina** &
\begin{tabular}{c} **Region** \\ **b. Retina** \\ \end{tabular} & **PCV** & **ILM** & **RPE** \\ \hline UNet & 0.9968 (0.008) & **0.9591 (0.074)** & **0.9944 (0.002)** & 0.9984 (0.002) & **1.33 (2.88)** & 0.50 (0.28) & 0.57 (0.29) \\ LFUNet & **0.9973 (0.006)** & 0.9590 (0.072) & 0.9942 (0.002) & **0.9985 (0.001)** & 1.50 (3.53) & **0.50 (0.12)** & **0.57 (0.24)** \\ Attention UNet & 0.9850 (0.030) & 0.8802 (0.186) & 0.9926 (0.004) & 0.9956 (0.010) & 4.02 (6.54) & 0.60 (0.26) & 0.80 (0.73) \\ ReLayNet & 0.9603 (0.054) & 0.8015 (0.240) & 0.9918 (0.004) & 0.9859 (0.025) & 12.63 (13.84) & 0.71 (0.59) & 0.96 (0.88) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dice score and Mean Absolute Error (with Standard Deviation) results of 4 class pixel segmentation with various neural network models after removing 3% of anomalous data samples and expanding data set with 4 augmentation techniques. The best results are highlighted in bold.