# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 4 Experiments and Results
### Results
We continued to survey the inpainting results using a multi-task learning framework based on edge awareness and organ boundary knowledge in the medical image. We achieved promising results when the PSNR metric value increased to 43.44 dB, which surpassed all remaining methods. Tables 3 and 6 show that our results still obtained the best value for the mask shape in an arbitrary form. The proposed method helped us achieve a PSNR value exceeding 38.06 dB compared with the highest value of the remaining methods of 37.20 dB belonging to the EdgeConnect method [4]. Our approach has outstanding practicality and outstanding performance from the above quantitative comparisons compared to inpainting methods in recent years. We also present qualitative comparisons in Figures 5 and 6. We apply a mask shape of a square in Figure 5 and then we reproduce images with very high authenticity. The structure of the right lung is preserved relatively intact while the remaining methods reproduce low plausible images. The other methods generate images that lost so much detailed information. The left lung in Figure 6 has degraded a lot when we use an arbitrary \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Mono-Task**}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Multi-Task with Edge** \\ **Information** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\
\begin{tabular}{c} **Multi-Task with Organs’** \\ **Boundary Information** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Multi-Task with Edge and** \\ **Boundary Information** (Ours) \\ \end{tabular} }} \\ \hline PSNR & 36.62 & 37.20 & 36.94 & 38.06 \\ SSIM & 0.9724 & 0.9745 & 0.9734 & 0.9746 \\ MSE & 69.78 & 69.25 & 63.73 & 50.49 \\ UQI & 0.9963 & 0.9962 & 0.9967 & 0.9972 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The quantitative comparison of PSNR/SSIM/MSE/UQI between multi-task and mono-task framework in property of arbitrary-shaped regions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Mono-Task**}} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Multi-Task with Edge** \\ **Information** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\
\begin{tabular}{c} **Multi-Task with Organs’** \\ **Boundary Information** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Multi-Task with Edge and** \\ **Boundary Information** \\ \end{tabular} }} \\ \hline PSNR & 40.85 & 42.32 & 42.77 & 43.44 \\ SSIM & 0.9797 & 0.9812 & 0.9817 & 0.9818 \\ MSE & 61.36 & 61.64 & 36.35 & 37.93 \\ UQI & 0.9946 & 0.9947 & 0.9963 & 0.9960 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The quantitative comparison of PSNR/SSIM/MSE/UQI between multi-task and mono-task framework in property of square-shaped regions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Source-Shaped Regions**}} & \multicolumn{2}{c}{**Arbitrary-Shaped Regions**} \\ \hline  & **w/o SRM** & **Ours** & **w/o SRM** & **Ours** \\ \hline PSNR & 41.98 & 43.44 & 36.99 & 38.06 \\ SSIM & 0.9804 & 0.9818 & 0.9726 & 0.9746 \\ MSE & 44.16 & 37.93 & 59.23 & 50.49 \\ UQI & 0.9954 & 0.9960 & 0.9967 & 0.9972 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The effect of SRM in our network. shaped mask. However, thanks to the multi-task framework based on edge awareness and organ boundary knowledge, our method can reproduce the image with a plausible result. Especially, the structures of the right lung and left lung are pretty well reconstructed. The left lung boundary area was reproduced quite sharply without any blurring or distortion, while the rest of the methods were not capable. Table 4 shows the comparison of results from different types of loss function using a discriminator. By using binary cross-entropy loss, our model can only generate results with 37.00 and 41.78 dB in PSNR with the property of arbitrary-shaped mask and square-shaped mask, respectively.