# Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks
## 7 Conclusions  
In this work, we have evaluated a set of methods employed for the segmentation of the preretinal space in optical coherence tomography images of the retina. We proposed an OCT image segmentation system that can help doctors automatically quantify morphological parameters of the preretinal space in healthy eyes and pathological cases of vitreomacular traction. Our approach provides robust end-to-end learning of preretinal space borders, with performance higher than in previous works. Employing CNN for this task does not require image preprocessing in the form of denoising, thresholding or other methods as is with standard computer vision algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Kernel [px]**} & \multicolumn{4}{c}{**Dice Score**} & \multicolumn{4}{c}{**MAE [px]**} \\ \hline
**Size** & **Area** & **Vitreous** & \
\begin{tabular}{c} **Preretinal** \\ **Space** \\ \end{tabular} & **Retina** &
\begin{tabular}{c} **Space** \\ **under Retina** \\ \end{tabular} & **PCV** & **ILM** & **RPE** & **TII [\%]** \\ \hline
3 \(\times\) 3 & 9 & 0.9968 & 0.9591 & 0.9944 & 0.9984 & 1.33 & 0.50 & 0.57 & 15.1 \\
5 \(\times\) 5 & 25 & 0.9980 & 0.9615 & 0.9945 & **0.9988** & 1.08 & 0.47 & 0.54 & 4.6 \\
7 \(\times\) 7 & 49 & **0.9981** & 0.9635 & **0.9948** & **0.9988** & 0.92 & **0.45** & **0.52** & 4.6 \\ \hline
5 \(\times\) 3 & 15 & 0.9978 & 0.9615 & 0.9944 & 0.9987 & 1.17 & 0.48 & 0.58 & 5.7 \\
7 \(\times\) 3 & 21 & 0.9981 & 0.9628 & 0.9946 & 0.9988 & 0.95 & 0.47 & 0.54 & 2.8 \\
9 \(\times\) 3 & 27 & **0.9981** & 0.9626 & 0.9947 & **0.9988** & 0.93 & 0.47 & **0.52** & 2.4 \\ \hline
3 \(\times\) 5 & 15 & 0.9977 & 0.9620 & 0.9945 & 0.9987 & 1.22 & 0.47 & 0.55 & 9.7 \\
3 \(\times\) 7 & 21 & 0.9980 & 0.9631 & 0.9946 & 0.9987 & 0.98 & 0.46 & 0.55 & 6.8 \\
3 \(\times\) 9 & 27 & **0.9981** & **0.9640** & 0.9946 & 0.9987 & **0.90** & 0.46 & 0.54 & 5.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Dice score and Mean Absolute Error results of 4 class pixel segmentation with various kernel sizes for UNet models after removing anomalous data samples and expanding data set with 4 augmentation techniques. Figure 9: Results of preretinal space segmentation calculated using a Dice score for layer segmentation performed with UNet various kernel sizes. We have shown the challenges associated with the segmentation of the preretinal space and proposed a solution in applying convolutional neural networks to this task. With quantitative and qualitative tests, we analyzed four neural network architectures, including UNet, LFUNet, Attention UNet, and ReLayNet for this task. Two standard metrics of Dice score and MAE were used for evaluation. An additional discussion on their interchangeability and limitation for preretinal space analysis was shown. The evaluation tests were conducted on a unique dataset of OCT images with 25 vitreomacular traction patients and 25 vitreomacular adhesion subjects. By performing the segmentation at a 2D level, thus utilizing 7050 images, we avoid computationally expensive 3D convolutions. In general, the original UNet and LFUNet have performed relatively well, correctly segmenting preretinal space borders, with Mean Absolute Error of 1.33 px and 1.5 px, respectively, whereas the Attention UNet and ReLayNet gave MAE results of 4.02 px and 12.63 px, respectively. Nevertheless, all networks faced the challenge of incorrect vertical topology associated with the semantic image segmentation, but unacceptable in biological image analysis. We have proposed two new approaches for improving the topological correctness of OCT image segmentation, namely two relative distance maps and the use of a non-typical convolution kernel. Extensive experiments for all network architectures show that both of the proposed relative distance maps tailored for preretinal space better preserve the correct layers' topology (improvement from 15.1% to 3.7% for UNet, and from 11.5% to 4.8% for LFUNet), than the state-of-the-art approach (9.4% for UNet and 6.3% for LFUNet).