# Multi-Task Learning for Medical Image Inpainting Based on Organ Boundary Awareness
## 4 Experiments and Results
### Results  
Our results in PSNR and MSE metrics are presented by graphs which are shown in Figures 3 and 4. Figure 3 shows the PSNR score of our method for a square-shaped and arbitrary-shaped masked image compared with the others. The higher value is the better. Figure 4 presents the MSE score of our method for a square-shaped and arbitrary-shaped masked image compared with the others. The lower value is the better. For square-shaped inpainting, the results are presented in Figure 5 and Table 2, respectively. Partial convolution [13] resulted in the worst inpainting results from both a quantitative and qualitative perspective. The proposed method achieved the highest performance compared to the others. In Figure 6 and Table 3, the qualitative and quantitative results for \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & [4] & [13] & [14] & [15] & [16] & [18] & **Ours** \\ \hline PSNR & 40.63 & 22.34 & 36.57 & 34.23 & 43.00 & 37.97 & 43.44 \\ SSIM & 0.9785 & 0.2335 & 0.9002 & 0.7032 & 0.9811 & 0.9764 & 0.9818 \\ MSE & 59.41 & 1223.08 & 97.75 & 118.80 & 38.84 & 93.30 & 37.93 \\ UQI & 0.9938 & 0.2666 & 0.8767 & 0.9393 & 0.9951 & 0.9899 & 0.9960 \\ \hline \end{tabular}
\end{table} 
Table 2: The quantitative comparison of PSNR/SSIM/MSE/UQI between proposed method and other methods in property of squared-shaped regions. The results from other methods are derived from using their public code. Figure 5: The inpainting results of our method for a square-shaped masked image compared with the others. (**a**) The masked image. (**b**) Result obtained from using public source code of method [15]. (**c**) Result obtained from using public source code of method [16]. (**d**) Result obtained from using public source code of method [4]. (**e**) Result obtained from using public source code of method [13]. (**f**) Result obtained from using public source code of method [14]. (**g**) Result obtained from using public source code of method [18]. (**h**) Result of our method. (**i**) Ground truth image. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Arbitrary-Shaped Regions**} & \multicolumn{3}{c}{**Square-Shaped Regions**} \\ \hline  & **BCE** & **MSE** & **Hinge (Ours)** & **BCE** & **MSE** & **Hinge (Ours)** \\ \hline PSNR & 37.00 & 37.58 & 38.06 & 41.78 & 42.51 & 43.44 \\ SSIM & 0.9723 & 0.9742 & 0.9746 & 0.9802 & 0.9812 & 0.9818 \\ MSE & 57.75 & 53.64 & 50.49 & 50.36 & 46.44 & 37.93 \\ UQI & 0.9965 & 0.9970 & 0.9972 & 0.9948 & 0.9953 & 0.9960 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effect of loss function using a discriminator. Figure 6: The inpainting results of our method for an arbitrary-shaped masked image compared with the others. (**a**) The masked image. (**b**) Result obtained from using public source code of method [15]. (**c**) Result obtained from using public source code of method [16]. (**d**) Result obtained from using public source code of method [4]. (**e**) Result obtained from using public source code of method [13]. (**f**) Result obtained from using public source code of method [14]. (**g**) Result obtained from using public source code of method [18]. (**h**) Result of our method. (**i**) Ground truth image. Tables 2 and 3 show the experimental results compared to other methods based on both arbitrary-shaped and square-shaped masks. Compared to recent inpainting studies, our method produced promising results. Particularly, we compared it to methods [13; 15] proposed in 2018, methods [4; 14; 16], introduced in 2019, and method [18] presented in 2020. From Tables 2 and 7, for square mask shapes, with using the only mono-task framework, our results are only 40.85 dB PSNR and lower than the method results from LBAM [16], with a PSNR value reaching 43 dB. Although we integrated the edge knowledge learning task or organ boundary awareness task, the results show very little performance increase with the PSNR metric increased to 42.32 and 42.77 dB, respectively. However, it does prove the positive value of adding the auxiliary task into the network.