# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results
### Comparison of Results of Split Experiments
This indicates that the segmentation results of the HR images were more accurate and finer at the organ level, and more closely matched the ground truth labels, while the segmentation results of the LR images were lower and had obvious deficiencies. In summary, HR images showed obvious advantages in the abdominal organ segmentation task with more accurate and clearer segmentation results, which provides important support and guidance for medical diagnosis and research based on PAI. Figure 9 shows the segmentation results of HR-PAI on multiple networks. By evaluating the images, it can be concluded that the segmentation effect of different networks varied significantly, FCN had obvious deficiencies in segmenting edges and morphology, while SegNet could not distinguish multiple types of organs well. U-Net had a better segmentation effect, but there was pixel confusion, and some regions were not segmented accurately. DeepLabv3+, on the other hand, showed excellent segmentation results, highly similar to the ground truth labels with clear morphological edges, showing the best segmentation results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Accuracy** & **IoU** & **BFScore** & **Dicesorce** \\ \hline \multirow{3}{*}{HR-PAI} & Liver & 0.890 & 0.829 & 0.703 & 0.801 \\  & Right kidney & 0.908 & 0.847 & 0.901 & 0.811 \\  & Left kidney & 0.887 & 0.831 & 0.893 & 0.773 \\  & Spleen & 0.846 & 0.800 & 0.845 & 0.772 \\ \hline \multirow{3}{*}{LR-PAI} & Liver & 0.820 & 0.772 & 0.611 & 0.736 \\  & Right kidney & 0.821 & 0.785 & 0.819 & 0.757 \\ \cline{1-1}  & Left kidney & 0.776 & 0.726 & 0.773 & 0.724 \\ \cline{1-1}  & Spleen & 0.729 & 0.738 & 0.742 & 0.688 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation indexes for segmentation of each organ in HR-PAI and LR-PAI. Figure 8: Segmentation Dice coefficients for each organ in HR-PAI and LR-PAI. Table 8 demonstrates the segmentation evaluation metrics of HR-PAI on the test set. DeepLabv3+ showed the best results in five metrics, namely overall pixel accuracy, average pixel accuracy, mean IoU, weighted IoU, and mean BFScore, which reached 0.992, 0.903, 0.856, 0.984, and 0.880, respectively. Table 9 and Figure 10 show the Dice coefficients of the segmentation results for each network. The box diagram of the SegNet part intersects with 0 because SegNet cannot distinguish multiple types of organs well in the segmentation experiment, and the Dice coefficient of other types of organs was 0. The Dice coefficients of DeepLabv3+ were significantly higher than those of other networks, indicating that it achieved the best results in the segmentation tasks of different organs. Table 10 shows the significant difference analysis results of DeepLabv3+ and other segmentation methods in the Dice coefficients of various organs in HR-PAI. The \(t\)-test results for DeepLabv3+ and U-Net on the liver and right kidney showed larger \(p\)-values. This could be because U-Net had a relatively better segmentation effect on these particular organs, leading to fewer significant differences. However, from the overall data, the \(p\)-values calculated by the other methods of difference analysis were much smaller than the significance level \(\alpha=0.05\), which indicates that there were significant differences between DeepLabv3 + and other methods. In summary, DeepLabv3 + not only had high accuracy and reliability in HR-PAI segmentation, but also could