## 3 Results  
The accuracy of the predicted regions of atrophy was evaluated using the median Dice coefficient and pixel accuracy of the eyes tested. Tables 1 and 2 show the median Dice coefficients and median pixel accuracies for the prediction of atrophy at a six-month follow-up for feature maps generated from several different regions. Tables 3 and 4 show the same for a twelve-month follow-up. Also included in these tables are the Dice coefficients and pixel accuracies calculated from the activations of the individual component U-Nets after being passed through a sigmoid function. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **ELM-EZ** & **EZ-IRPE** & **IRPE-ORPE** & **ORPE-C-S** & **ELM-IRPE** & **EZ \(\pm\) 5** \\ \hline Ensemble & 0.824 & 0.804 & 0.803 & 0.706 & 0.830 & 0.803 \\ \hline Mean Intensity & 0.196 & 0.126 & 0.344 & 0.259 & 0.349 & 0.260 \\ Standard Deviation & 0.558 & 0.694 & 0.199 & 0.443 & 0.303 & 0.430 \\ Maximum Intensity & 0.236 & 0.241 & 0.324 & 0.405 & 0.161 & 0.076 \\ Minimum Intensity & 0.425 & 0.089 & 0.297 & 0.179 & 0.327 & 0.077 \\ Median Intensity & 0.341 & 0.283 & 0.252 & 0.267 & 0.098 & 0.113 \\ Kurtosis & 0.771 & 0.240 & 0.592 & 0.293 & 0.451 & 0.219 \\ Skewness & 0.479 & 0.521 & 0.257 & 0.410 & 0.445 & 0.508 \\ Gray-Level Entropy & 0.247 & 0.276 & 0.413 & 0.016 & 0.677 & 0.237 \\ Thickness & 0.677 & 0.360 & 0.687 & & 0.568 & \\ \hline \end{tabular}
\end{table} 
Table 1: Ensemble neural network and component U-Net median Dice coefficients for six-month prediction. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **ELM-EZ** & **EZ-IRPE** & **IRPE-ORPE** & **ORPE-C-S** & **ELM-IRPE** & **EZ \(\pm\) 5** \\ \hline Ensemble & 0.977 & 0.973 & 0.974 & 0.964 & 0.980 & 0.971 \\ \hline Mean Intensity & 0.921 & 0.834 & 0.873 & 0.902 & 0.845 & 0.854 \\ Standard Deviation & 0.902 & 0.958 & 0.885 & 0.897 & 0.782 & 0.881 \\ Maximum Intensity & 0.940 & 0.741 & 0.943 & 0.923 & 0.793 & 0.848 \\ Minimum Intensity & 0.920 & 0.677 & 0.894 & 0.889 & 0.912 & 0.792 \\ Median Intensity & 0.901 & 0.590 & 0.928 & 0.835 & 0.649 & 0.709 \\ Kurtosis & 0.971 & 0.842 & 0.933 & 0.887 & 0.858 & 0.823 \\ Skewness & 0.961 & 0.939 & 0.809 & 0.792 & 0.914 & 0.892 \\ Gray-Level Entropy & 0.947 & 0.895 & 0.853 & 0.808 & 0.948 & 0.686 \\ Thickness & 0.950 & 0.856 & 0.940 & & 0.910 \\ \hline \end{tabular}
\end{table} 
Table 2: Ensemble neural network and component U-Net median pixel accuracy for six-month prediction. From these results, it was examined whether using multiple feature maps resulted in significant improvement and whether the choice of region had a significant impact on performance. For each of the ELM-EZ, EZ-IRPE, IRPE-ORPE, ELM-IRPE, and EZ \(\pm\) 5 regions, the Wilcoxon signed-rank test resulted in \(p\)-values less than 0.05 for both six-month and twelve-month predictions when comparing both Dice coefficient and pixel accuracy for neural networks using all feature maps versus neural networks using only the mean intensity feature map, indicating a significant difference in performance when adding additional feature maps. For a comparison between the choice of regions, the ELM-IRPE and EZ \(\pm\) 5 regions were examined as representative of two different strategies for selecting a region. Significant differences were found when comparing \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **ELM-IRPE** & **EZ \(\pm\) 5** & _p-Value_ \\ \hline All Features Dice Coefficient & 0.823 & 0.791 & 0.003 \\ Mean Intensity Dice Coefficient & 0.762 & 0.700 & 0.395 \\ _p-value_ & \textless{}0.001 & \textless{}0.001 & \\ \hline All Features Pixel Accuracy & 0.977 & 0.970 & 0.076 \\ Mean Intensity Pixel Accuracy & 0.969 & 0.956 & 0.003 \\ _p-value_ & 0.001 & 0.001 & \\ \hline \end{tabular}
\end{table} 
Table 6: Mean intensity vs. many features for twelve-month prediction.