## 3 Results
The meanIOU computes the average value of IOU for every image (Equation (8)) over each class \(c\in Classes\) Equation (9). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **Background** & **Healthy Tissue** & **Whole Tumor** \\ \hline FCN-ResNet50 & 0.9965 & 0.9730 & 0.9068 \\ FCN-ResNet101 & 0.9969 & 0.9744 & 0.8980 \\ PSP-ResNet50 & 0.9969 & 0.9746 & 0.9009 \\ PSP-ResNet101 & 0.9961 & 0.9701 & 0.8911 \\ DeepLab-RN50 & 0.9929 & 0.9599 & 0.8889 \\ DeepL-ResNet101 & 0.9967 & 0.9726 & 0.9030 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dice for Background (BG), Healthy Tissue (HT), and Whole Tumor (WT). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Architecture** & **Edema** & **Active Tumor** &
\begin{tabular}{c} **Necrosis/** \\ **Non-Enhanced Tumor** \\ \end{tabular} \\ \hline FCN-ResNet50 & 0.6963 & 0.6970 & 0.4617 \\ FCN-ResNet101 & 0.7150 & 0.7126 & 0.4435 \\ PSP-ResNet50 & 0.6589 & 0.7121 & 0.4326 \\ PSP-ResNet101 & 0.7521 & 0.6987 & 0.3949 \\ DeepLab-ResNet50 & 0.6740 & 0.6780 & 0.3859 \\ DeepL-ResNet101 & 0.6976 & 0.7370 & 0.4763 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dice for Different Types of Tumor Tissues. \[IOU=\frac{|Pred\cap GT|}{|Pred\cup GT|}\enspace, \tag{7}\] \[IOU^{I}=\frac{1}{N}\sum_{i\in I}\frac{|Pred_{i}\cap GT_{i}|}{|Pred_{i }\cup GT_{i}|}\enspace,\] (8) \[mIOU=\frac{1}{|Class|}\sum_{c\in Classes}IOU^{I}_{c}=\frac{1}{|Class|} \cdot\frac{1}{N}\sum_{c\in Classes}\sum_{i\in I}\frac{|Pred_{i}\cap GT_{i}|}{| Pred_{i}\cup GT_{i}|}\enspace, \tag{9}\]  
This metric is the same as the Jaccard index (Equation (10)).The Jaccard index can be expressed not only by the IOU but also as a fraction of TPR over TPR + FPR + FNR. \[Jaccard=\frac{|Pred\cap GT|}{|Pred\cup GT|}=\frac{TPR}{TPR+FPR+FNR}\enspace, \tag{10}\]  
The optimization parameters [55] that had to be set up to give reasonable and quite restricted intervals for them were the optimization function, learning rate, weight decay, momentum, and minibatch-size. Firstly, we set the mini-batch size between 16 and 64. The optimization functions added in the optimization process were MB-SGD, SGD with momentum, AdaDelta, and Adagrad. SGD for mini-batches takes the gradient step for a mini-batch with a regularization term called weight decay (=0.0001) multiplied by the weight and added to the gradient. SGD with momentum reduces the fluctuation towards the optimal value by adding the momentum term. AdaGrad (Adapted Gradient Descent) modifies the learning rate in each iteration biased towards the past gradient of that parameter. Instead of the past gradient in AdaGrad, AdaDelta modifies the learning rate by the average over the past squared gradients of a weight. The learning rate scheduler controls the decrease in the initial learning rate over time over the progress of the epochs. The learning rate is multiplied by a factor of 0.1 after a given number of epochs (=10). The early stopping algorithm stops a training job if certain stopping conditions are met. The minimum number of epochs was set to 5, early stopping patience was 4, and early stopping tolerance was 0.001. Hyperparameter optimization was conducted for only one architecture of the six studied, namely, FCN-ResNet50. The best parameters obtained via hyperparameter optimization were minibatch-size = 18, learning rate = 0.0009, weight decay = 0.0114, momentum = 0.803, and the AdaGrad optimization method. Several training jobs with different parameter setups stopped before the end of the job, recognizing at a very early epoch that their process involving the optimization metric is smaller than the best thus far. The validation mIOU for the best hyperparameter setup was 0.7732. This mIOU is a value similar to the one obtained for the first variant of FCN-ResNet50 without hyperparameter optimization (mIOU = 0.7649).