## 4 Discussion  
In the BraTS Challenge, the tumor tissue types are grouped into different tumor regions. The NEC/NET results are not considered separately. The Leaderboard results show the Enhancing Tumor (ET = class 5), the Whole Tumor (WT = classes \(2/4+3+5\)),  
Figure 12: Segmentation Results of the Ensemble Model: (**a**) original image; (**b**) ground truth with annotation contours; (**c**) ensemble segmentation; (**d**) ensemble segmentation with annotation contours; (**e**) ensemble segmentation of ED (turquoise), AT (yellow), NEC (red), Annotation Contours (black). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **No. of Epochs** & **Training Time [h]** & **GPU Mem.** \\ \hline FCN-ResNet50 & 86 & 7 & 16 GB \\ FCN-ResNet101 & 80 & 8.5 & 16 GB \\ PSP-ResNet50 & 100 & 8 & 16 GB \\ PSP-ResNet101 & 74 & 8 & 16 GB \\ DeepLab-ResNet50 & 49 & 7.5 & 16 GB \\ DeepL-ResNet101 & 66 & 10 & 16 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Training Time of the 6 Networks. and Tumor Core (\(2/4+5=\text{NEC}/\text{NET}+\text{AT}\)). The Leaderboard of the BraTS competition shows the results of the various participating teams, measuring the Dice scores on the so-called validation dataset. At the BraTS 2020 competition, there were 292 teams present on the Leaderboard. The statistics related to the average results are detailed in Table 9 and Figure 13. The average is marked by X, the median is the center-line, and the Q1 and Q3 values are the lower and upper margins of the box. The mean and Q3-75% quartiles are relevant from our perspective. Our results regarding ET, WT, and TC, as presented in Table 10, are far better than the average of the Leaderboard and are comparable to the Q3 quartile results. This means that our results are among the first 25% of the Leaderboard. We compare our results obtained on the test set, which is 20% taken separately from the BraTS dataset. The Leaderboard results are measured on a validation dataset not publicly available. If we consider both datasets being sufficiently general, the results are comparable. Figure 13: Leaderboard Dice Scores for ET, WT, and TC: the mean is denoted by X, the median is the center-line of the box, and Q1 and Q3 are the top and bottom edges of the rectangles. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Statistic** & **Enhanced Tumor** & **Whole Tumor** & **Tumor Core** \\ \hline Mean & 0.6638 & 0.8275 & 0.7233 \\ StdDev & 0.1942 & 0.1859 & 0.2011 \\ Median & 0.7289 & 0.8882 & 0.7964 \\ Q1 & 0.6664 & 0.8573 & 0.7168 \\ Q3 & 0.7737 & 0.9016 & 0.8296 \\ Max & 0.8802 & 0.9246 & 0.9289 \\ Min & 0.1040 & 0.0000 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Dice on the BraTS 2020 Leaderboard. Table 10 presents our results from the six different architectures and the BraTS tumor regions. The three tumor regions are ET (Enhanced Tumor), Whole Tumor (WT), and TC (Tumor Core). The best Dice scores we obtained are about 90% for WT, 84% for TC, and 78% for ET. The best results obtained in the BraTS competition are presented in Table 11. These are slightly worse than the Leaderboard maximums. The competition score is a result of a one-time experiment, making the progressive architecture adjustments impossible, whereas the leaderboard score is the best score achieved by a team. Therefore, the best results at competitions are 7-8% worse than the best results on the leaderboard. Comparing the six different architectures (Table 9) with the ensemble model, we can see a 2% decrease in the Dice score for WT but a 2% increase for ET and TC (Table 12). Overall, we obtained fairly good results that are comparable to the BraTS Leaderboard results. We suggest applying the best model for WT (DeepLab-ResNet101), and after that, finetuning the contour regions between different tumor tissues with the ensemble model. However, our results are quite competitive, and they will be improved in several ways. We propose some aspects for future improvement and further development. The quality and resolution of the images is not standardized.