# Deep Learning-Based Super-Resolution Reconstruction and Segmentation of Photoacoustic Images
## 4 Discussion  
In this paper, a new network architecture, EDSR-M, was proposed to solve the problem of unclear imaging of PAI due to the imaging principle, to achieve high-quality super-resolution reconstruction of PAI, and to combine with DeepLabv3+ for image segmentation. The effectiveness of the method was verified by experiments and compared with that of other reconstruction and segmentation methods. The results of the reconstruction experiments show that EDSR-M outperformed other reconstruction methods both numerically and in terms of image effect, with average improvements of 11.02% in PSNR and 4.13% in SSIM, as well as superior performance in fine structure and texture reconstruction. The segmentation impact of HR-PAI after reconstruction by this model was much superior to that of LR-PAI according to the segmentation experiment findings. The segmentation results were enhanced by 8.27%, 6.20%, and 6.28% in terms of accuracy, mean IoU, and mean BFScore, respectively. The LR-PAI segmentation results exhibited fuzzy edges and confusing pixels, whereas the HR-PAI segmentation results had distinct edges and excellent accuracy. Furthermore, DeepLabv3+ performed well in PAI segmentation, with signifi \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Liver** & \begin{tabular}{c} **Right** \\ **Kidney** \\ \end{tabular} & \
\begin{tabular}{c} **Left Kidney** \\ \end{tabular} &
\begin{tabular}{c} **Spleen** \\ \end{tabular} \\ \hline \multirow{3}{*}{FCN} & \(t\)-test & 2.30 \(\times\)\(10^{-12}\) & 1.93 \(\times\)\(10^{-9}\) & 1.73 \(\times\)\(10^{-9}\) & 6.87 \(\times\)\(10^{-10}\) \\  & ANOVA & 1.56 \(\times\)\(10^{-7}\) & 8.26 \(\times\)\(10^{-7}\) & 2.09 \(\times\)\(10^{-10}\) & 1.40 \(\times\)\(10^{-8}\) \\  & MANOVA \(\star\) & & & 3.15 \(\times\)\(10^{-9}\) & \\ \hline \multirow{3}{*}{SegNet} & \(t\)-test & 9.51 \(\times\)\(10^{-15}\) & 3.10 \(\times\)\(10^{-29}\) & 7.22 \(\times\)\(10^{-29}\) & 4.60 \(\times\)\(10^{-22}\) \\  & ANOVA & 5.13 \(\times\)\(10^{-11}\) & 1.23 \(\times\)\(10^{-36}\) & 3.81 \(\times\)\(10^{-32}\) & 9.74 \(\times\)\(10^{-27}\) \\  & MANOVA \(\star\) & & & 7.35 \(\times\)\(10^{-29}\) & \\ \hline \multirow{3}{*}{U-Net} & \(t\)-test & 0.572 & 0.115 & 8.20 \(\times\)\(10^{-3}\) & 1.157 \(\times\)\(10^{-3}\) \\  & ANOVA & 2.63 \(\times\)\(10^{-3}\) & 8.20 \(\times\)\(10^{-3}\) & 4.81 \(\times\)\(10^{-5}\) & 3.28 \(\times\)\(10^{-7}\) \\ \cline{1-1}  & MANOVA \(\star\) & & & 1.43 \(\times\)\(10^{-7}\) & \\ \hline \hline \end{tabular}  
*MANOVA analyzed the Dice coefficients of each organ as a whole, significance level \(\alpha=0.05\).  
\end{table} 
Table 10: Analysis of significant differences in Dice coefficients of various organs in HR-PAI between DeepLabv3+ and other segmentation methods. Figure 10: The Dice coefficients of each organ in HR-PAI by different segmentation methods. cantly higher accuracy, IoU, and BFScore when compared to those of other approaches, demonstrating the relevance of increasing PAI resolution for better segmentation outcomes. In summary, deep learning-based reconstruction methods can effectively improve the reconstruction quality of PAI, which helps to enhance the accuracy and reliability of medical diagnosis. HR images provide more accurate labels, which can better help doctors understand the PAI and provide more valuable information for clinical diagnosis. In future research, the performance of the method with real PAI data and other medical images will be further validated to improve the generalization ability and robustness of the algorithm and to promote its wide application in the field of medical imaging. Conceptualization, Y.J. ; methodology, Y.J. ; software, Y.J. ; validation, Y.C., R.H., Y.L., S.Y. and J.Z. ; data curation, Y.J. ; writing--original draft preparation, Y.J. ; writing--review and editing, Y.J., Y.C., R.H. and J.Z. ; supervision, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.