# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 6 Results
### Effect of Data Augmentation
\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 Graph search & 0.9853 (0.028) & 0.8217 (0.206) & 0.9408 (0.158) & 0.9900 (0.022) & 6.76 (11.1) & 4.66 (9.9) & 4.71 (13.2) \\ UNet & 0.9954 (0.010) & 0.9486 (0.084) & **0.9931 (0.002)** & 0.9978 (0.002) & 2.19 (4.55) & **0.54 (0.17)** & **0.73 (0.38)** \\ LFUNet & **0.9958 (0.010)** & **0.9499 (0.081)** & 0.9930 (0.002) & **0.9980 (0.002)** & **2.05 (4.41)** & 0.56 (0.26) & 0.76 (0.52) \\ Attention UNet & 0.9842 (0.029) & 0.8688 (0.197) & 0.9924 (0.004) & 0.9959 (0.008) & 4.89 (7.89) & 0.62 (0.29) & 0.82 (0.72) \\ ReLayNet & 0.9563 (0.056) & 0.7966 (0.232) & 0.9908 (0.005) & 0.9838 (0.026) & 15.94 (18.80) & 0.76 (0.79) & 1.17 (1.11) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 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. The best results are highlighted in bold. occurrences are less common with UNet and LFUNet; nevertheless, those architectures are not immune to them, and further improvements are necessary. Both UNet and LFUNet correctly learn the preferential and retinal space borders regardless of the PCV intensity in the image, which is a significant improvement over the graph search-based method. Visually, those networks perform very well for both VMT and VMA cases. Furthermore, their accuracy is not affected by the placement of preretinal space in the image or the area it spans. Compared with the classical approach based on image intensity gradient, the neural network learns smooth region borders and is not affected by slight local intensity variations--it can robustly generalize the preretinal space structure. Moreover, the graph search-based approach has difficulty correctly detecting the PCV border whenever it connects with the ILM line. This is a distinct disadvantage compared to the neural network methods that do not present such hindering. On the other hand, it should be noted that in cases when the preretinal space takes only a narrow area the image (example in the last row in Figure 8), a slight thickening of preretinal space in the prediction mask (e.g., region border 1 px higher) would significantly affect the Dice score (e.g., decreasing it by half). Such lowering of metric evaluation value may lead to the assumption that the designed network is not performing well. Nevertheless, in such a case, the MAE value would be relatively small. This is why the Dice score for regions spanning various area sizes (and particularly for imbalanced classes of small regions) should not be a sole indicator of the network's performance. Figure 7: Box plots for Dice score of preretinal space for various neural network architectures. Figure 8: Example B-scans with corresponding reference mask and segmentation results for four analyzed neural networks and graph search-based method. Each shade in the segmentation mask represents a separate class.