# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 3 Results
### Comparison of Results of Split Experiments  
In the image segmentation experiments, the segmentation results of HR-PAI were compared with those of LR-PAI using DeepLabv3+ under the same training set (128 sensors). It was also compared with existing image deep-learning segmentation methods (FCN, SegNet, and U-Net). The predicted segmentation labels were compared with ground truth images using accuracy, IoU, BFScore, and the Dice coefficient as quantitative metrics of image segmentation quality. Meanwhile, the Dice coefficients of the segmentation results of each organ were analyzed for significant differences using several statistical analysis methods, including the \(t\)-test, ANOVA, and MANOVA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **TR** & **Bicubic** & **SRCNN** & **FSRCNN** & **VDSR** \\ \hline \multirow{2}{*}{PSNR} & \(t\)-test & \(6.91\times 10^{-43}\) & \(5.73\times 10^{-44}\) & \(3.34\times 10^{-40}\) & \(5.00\times 10^{-20}\) & \(4.14\times 10^{-21}\) \\  & ANOVA & \(3.50\times 10^{-42}\) & \(1.23\times 10^{-43}\) & \(1.27\times 10^{-18}\) & \(3.10\times 10^{-10}\) & \(8.12\times 10^{-09}\) \\ \hline \multirow{2}{*}{SSIM} & \(t\)-test & \(7.01\times 10^{-63}\) & \(5.15\times 10^{-63}\) & \(6.07\times 10^{-76}\) & \(1.24\times 10^{-62}\) & \(4.04\times 10^{-46}\) \\  & ANOVA & \(1.83\times 10^{-29}\) & \(9.01\times 10^{-31}\) & \(1.14\times 10^{-57}\) & \(6.31\times 10^{-13}\) & \(1.70\times 10^{-08}\) \\ \hline \multicolumn{2}{c}{MANOVA \({}^{*}\)} & \(1.90\times 10^{-267}\) & \(1.62\times 10^{-267}\) & \(1.17\times 10^{-266}\) & \(5.82\times 10^{-267}\) & \(2.56\times 10^{-268}\) \\ \hline \hline \end{tabular} \({}^{*}\)MANOVA analyzed PSNR and SSIM as a whole, significance level \(\alpha=0.05\).  
\end{table} 
Table 4: Analysis of significant differences between EDSR-M and other reconstruction methods in PSNR and SSIM (128 sensors). Figure 6: Comparison of super-resolution reconstruction performance of PAI by different methods (128 sensors). (**a**) PSNR of different methods’ reconstruction results; (**b**) SSIM of different methods’ reconstruction results. Figure 7 shows the segmentation results of HR-PAI and LR-PAI on a single image, and evaluating the segmentation results, it can be observed that the segmentation effect of HR images was significantly better than that of LR images. The segmented labels of the HR image were highly consistent with the ground truth labels with clear morphological edges, presenting excellent segmentation precision and accuracy. On the contrary, the segmented labels of the LR image showed obvious confusion, with pixel confusion between labels and blurred edges, which could not correctly capture the subtle features in the image. Table 5 shows the segmentation effect of HR-PAI and LR-PAI in the test set. The overall segmentation effect of the HR image was significantly better than that of the LR image. The overall pixel accuracy of the HR image reached 0.992, the average pixel accuracy was 0.903, the mean IoU was 0.856, the weighted IoU was 0.984, and the BFScore was 0.880, whereas the corresponding metrics for the LR image were 0.986, 0.834, 0.806, 0.954, and 0.828, respectively. As seen in Table 6, the HR images had better pixel accuracy, IoU, and BFScore on all organ types than the LR images, and the average Dice coefficient was correspondingly higher. Table 7 shows the significant difference analysis results of the Dice coefficients of each organ segmentation of HR-PAI and LR-PAI. The \(p\)-values calculated by all difference analysis methods were smaller than the significance level \(\alpha=0.05\), which indicates that there were significant differences between HR images and LR images in the segmentation of different organs. In Figure 8, the Dice scores for each organ category on the test set are shown, where the Dice scores for all HR images were overall higher than those for LR images.