# Generation of Synthetic Rat Brain MRI Scans with a 3D Enhanced Alpha Generative Adversarial Network
## 3 Results
For each rater, the first three values are the number of failed answers, e.g., \(\alpha\)-WGANSigmaRat1 = 13 means that 13 scans generated by the \(\alpha\)-WGANSigmaRat1 model were misclassified, and the last value is the number of correct answers. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Results** & **MS-SSIM** & **NCC \(\uparrow\)** & **MAE \(\downarrow\)** & **MMD \(\downarrow\)** \\ \hline \multirow{2}{*}{\(\alpha\)-WGAN\_ADNI [26]} & 0.6860 & 0.7241 & 0.0316 & 779.4653 \\  & \(\pm\)0.0066 & \(\pm\)0.0071 & \(\pm\)0.0004 & \(\pm\)27.2016 \\ \hline \multirow{2}{*}{\(\alpha\)-WGANSigmaRat1} & **0.8118** & **0.7887** & **0.0305** & **753.1584** \\  & **\(\pm\)0.0051** & **\(\pm\)0.0041** & **\(\pm\)0.0004** & **\(\pm\)24.8816** \\ \hline \multirow{2}{*}{\(\alpha\)-WGANSigmaRat2} & 0.8236 & 0.7527 & 0.0325 & 819.3409 \\  & \(\pm\)0.0056 & \(\pm\)0.0037 & \(\pm\)0.0003 & \(\pm\)20.4437 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative results. For each cell, the first value is the mean and the second is the standard deviation. The best results are in bold for each metric. An up arrow \(\uparrow\) next to the metric name means that larger values are better, and a down arrow \(\downarrow\) means the opposite. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Rater**} \\ \cline{2-9}  & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} \\  & \multicolumn{2}{c}{**High**} & \multicolumn{2}{c}{**Low**} & \multicolumn{2}{c}{**Medium**} & \multicolumn{2}{c}{**Medium**} \\ \cline{2-9}  & **Real** & **Syn** & **Real** & **Syn** & **Real** & **Syn** & **Real** & **Syn** \\ \hline \(\alpha\)-WGANSigmaRat1 & 1 & **14** & 13 & **2** & 2 & **13** & 5 & **10** \\ \(\alpha\)-WGANSigmaRat2 & 0 & **15** & 8 & **7** & 4 & **11** & 8 & **7** \\ Real & **19** & 1 & **4** & 16 & **17** & 3 & **12** & 8 \\ \hline Right Answers & & 48 & & 13 & & 41 & & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Turing test results. The first two rows are the classification results of the scans generated by the \(\alpha\)-WGANSigmaRat1 and \(\alpha\)-WGANSigmaRat2 models, respectively, the third row is the classification results of the real scans, and in the last row are the number of right classifications. Syn are the Synthetic scans. The correct answers are in bold. The generated scans were also evaluated in a segmentation task to determine if the synthetic data could improve the results and if it performed better than traditional data augmentation. This test is more important than tricking experts, since synthetic data should be created with a finality, and here, it is the segmentation task. Using a DL model developed by [PERSON] (2018) [PERSON] (2018) [PERSON] (2018) for GM, WM and CSF segmentation, an experiment was conducted using the following datasets: D\({}_{1}\)\({}^{\text{174}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{174}}\); D\({}_{8}\)\({}^{\text{87}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\); D\({}_{8}\)\({}^{\text{87}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\); D\({}_{8}\)\({}^{\text{87}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\); D\({}_{8}\)\({}^{\text{86}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\); D\({}_{8}\)\({}^{\text{86}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\); D\({}_{8}\)\({}^{\text{86}}\) = (x\({}_{i}\), y\({}_{i}\))\({}_{i=1}\)\({}^{\text{87}}\). The number of scans in each dataset is a multiple of 174, since the maximum of labelled real scans--with existing segmentation masks of GM, WM and CSF--is the dataset D\({}_{1}\)\({}^{\text{174}}\). Only the \(\alpha\)-WGANSigmaRat2 model was used to generate synthetic scans because the raters involved in the Turing test, especially the first one, indicated that the scans generated with this model were more realistic, had less blur and more detail, and could perform better on the segmentation task than the \(\alpha\)-WGANSigmaRat1 model.