# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 6 Results
### Issues with Class Topology
#### 6.4.2 Improving Segmentation with Non-Typical Convolution Kernel  
Previously described experiments showed the best performance of segmenting pre-retinal space and PCV line when utilizing UNet architecture. Therefore, this network will be used for further improvement with various convolutional kernel sizes.  
Table 8 presents the effect of convolutional kernel size on the performance of preretinal space segmentation. Understandably, the average Dice score increases for every segmented region as the kernel size increases. The same observation can be made for the average MAE  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Network**} & \multirow{2}{*}{
\begin{tabular}{c} **Distance** \\ **Map** \\ \end{tabular} } & \multicolumn{4}{c}{**Dice Score**} & \multicolumn{4}{c}{**MAE [px]**} & \multirow{2}{*}{**TI [\%]**} \\ \cline{3-4} \cline{6-10}  & & **Vircreous** & & **Peretial Space** & & **Retina** & **Region b. Retiina** & **PCV** & **ILM** & **RPE** \\ \hline \multirow{4}{*}{UNet} & — & 0.9968 & 0.9591 & 0.9944 & 0.9984 & 1.33 & 0.50 & 0.57 & 15.1 \\  & BasicOrient & 0.9976 & 0.9606 & 0.9944 & **0.9987** & 1.42 & **0.47** & 0.56 & 4.8 \\  & CumSum & **0.9977** & **0.9609** & **0.9945** & **0.9987** & 1.25 & **0.47** & **0.55** & 6.8 \\  & 2 NetR & 0.9976 & 0.9601 & 0.9943 & 0.9986 & 1.48 & 0.48 & 0.59 & 9.4 \\  & 2 NetPR & 0.9975 & 0.9588 & 0.9943 & **0.9987** & **1.20** & 0.50 & 0.57 & **3.7** \\ \hline \multirow{4}{*}{LFUNet} & — & 0.9973 & 0.9590 & 0.9942 & 0.9985 & 1.50 & 0.50 & 0.57 & 11.5 \\  & BasicOrient & 0.9975 & 0.9583 & 0.9942 & **0.9987** & 1.58 & 0.49 & 0.58 & 8.8 \\  & CumSum & **0.9977** & **0.9605** & **0.9944** & **0.9987** & **1.22** & **0.47** & **0.56** & **4.8** \\  & 2 NetR & 0.9974 & 0.9581 & **0.9944** & **0.9987** & 1.64 & 0.48 & **0.56** & 6.3 \\  & 2 NetPR & 0.9973 & 0.9571 & 0.9943 & **0.9987** & 1.26 & 0.51 & **0.56** & 5.0 \\ \hline \multirow{4}{*}{AttUNet} & — & 0.9850 & 0.8802 & **0.9926** & 0.9956 & 4.02 & 0.60 & 0.80 & 50.5 \\  & BasicOrient & 0.9824 & 0.8551 & 0.9880 & 0.9962 & 7.31 & 0.87 & 2.05 & 52.9 \\  & CumSum & 0.9886 & 0.9025 & **0.9926** & **0.9981** & 4.50 & **0.59** & **0.79** & 38.7 \\  & 2 NetR & 0.9860 & 0.8675 & 0.9920 & 0.9980 & 4.74 & 0.72 & 0.98 & 44.1 \\  & 2 NetPR & **0.9963** & **0.9508** & **0.9926** & **0.9981** & **2.04** & 0.65 & 0.86 & **12.8** \\ \hline \multirow{4}{*}{ReLayNet} & — & 0.9603 & 0.8015 & 0.9918 & 0.9859 & 12.63 & 0.71 & 0.96 & 88.3 \\  & BasicOrient & 0.9614 & 0.7528 & 0.9771 & 0.9930 & 20.00 & 0.59 & 0.79 & 92.0 \\ \cline{1-1}  & CumSum & 0.9730 & 0.7937 & 0.9906 & 0.9976 & 15.06 & 0.79 & 1.00 & 79.5 \\ \cline{1-1}  & 2 NetR & 0.9651 & 0.7594 & 0.9905 & 0.9974 & 18.55 & 0.70 & 1.11 & 88.1 \\ \cline{1-1}  & 2 NetPR & **0.9968** & **0.9544** & **0.9927** & **0.9981** & **1.42** & **0.58** & **0.81** & **10.8** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dice score and Mean Absolute Error results of 4 class pixel segmentation with various distance maps for models with \(3\times 3\) px kernel size and 32 initial features after removing anomalous data samples and expanding data set with 4 augmentation techniques.  
for all searched borders. Square kernels provide the best performance in terms of both MAE and Dice scores for the retina borders. Interestingly, the best result of preretinal space area and borders are obtained with a horizontal kernel of the size \(3\times 9\) px. From the numerical data and averaged results of Dice scores presented in Figure 9 it can be concluded that rectangular kernels (regardless of their orientation) give better results than the square ones when segmenting preretinal space. Additionally, even a kernel of size \(3\times 5\) or \(5\times 3\) px performs better than a square kernel of \(5\times 5\) px (which spans a greater area).