## 4 Discussion  
We confirmed the superior performance of our proposed model using DSC scores in testing. We further investigate the result with or without dissimilarity coefficient by running the test with exp_model_1 and exp_model_2. The exp_model_1 and the proposed model have the dissimilarity coefficient as a loss function, along with the DSC. The only difference is that the model is updated once or alternatively for those loss functions. From Tables 1 and 2 it is visible that the network with the information about the dissimilarity performed better than using DSC alone. If we compare the result of the models exp_model_1 with dissimilarity information and exp_model_2 without dissimilarity information, the network's score is improved by up to 3%. In all three planes and testing subjects, the models with our proposed additional loss function outperformed the model with only the DSC loss function. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Axial**} \\ \hline
**Model** & **CSF** & **GM** & **WM** \\ \hline Exp\_model\_1 & 0.0991 & 0.0958 & 0.1164 \\ Exp\_model\_2 & 0.1075 & 0.1057 & 0.1237 \\ Proposed model & 0.0907 & 0.0831 & 0.0913 \\ \hline \multicolumn{3}{c}{Coronal} \\ \hline Exp\_model\_1 & 0.0924 & 0.0803 & 0.0688 \\ Exp\_model\_2 & 0.0947 & 0.0862 & 0.0790 \\ Proposed model & 0.0890 & 0.0784 & 0.0688 \\ \hline \multicolumn{3}{c}{Sagittal} \\ \hline Exp\_model\_1 & 0.0885 & 0.0811 & 0.0821 \\ Exp\_model\_2 & 0.0962 & 0.0938 & 0.1037 \\ Proposed model & 0.0877 & 0.0806 & 0.0829 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: VOE comparison of segmentation results from different experimental models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Axial**} \\ \hline
**Model** & **CSF** & **GM** & **WM** \\ \hline Exp\_model\_1 & 0.9463 \(\pm\) 0.0445 & 0.9472 \(\pm\) 0.0591 & 0.9304 \(\pm\) 0.1098 \\ Exp\_model\_2 & 0.9399 \(\pm\) 0.0703 & 0.9390 \(\pm\) 0.0902 & 0.9235 \(\pm\) 0.1271 \\ Proposed model & 0.9518 \(\pm\) 0.0200 & 0.9555 \(\pm\) 0.0374 & 0.9467 \(\pm\) 0.09458 \\ \hline \multicolumn{3}{c}{Coronal} \\ \hline Exp\_model\_1 & 0.9511 \(\pm\) 0.0220 & 0.9578 \(\pm\) 0.0198 & 0.9635 \(\pm\) 0.0312 \\ Exp\_model\_2 & 0.9497 \(\pm\) 0.0252 & 0.9541 \(\pm\) 0.0326 & 0.9564 \(\pm\) 0.0585 \\ Proposed model & 0.9530 \(\pm\) 0.0212 & 0.9589 \(\pm\) 0.0192 & 0.9634 \(\pm\) 0.0340 \\ \hline \multicolumn{3}{c}{Sagittal} \\ \hline Exp\_model\_1 & 0.9534 \(\pm\) 0.0198 & 0.9573 \(\pm\) 0.0223 & 0.9551 \(\pm\) 0.0538 \\ Exp\_model\_2 & 0.9487 \(\pm\) 0.0303 & 0.9492 \(\pm\) 0.0457 & 0.9395 \(\pm\) 0.0926 \\ Proposed model & 0.9537 \(\pm\) 0.0199 & 0.9575 \(\pm\) 0.0232 & 0.9546 \(\pm\) 0.0553 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DSC score comparison of segmentation results of all testing subjects (average of DSC scores in total testing subjects). We compared our proposed method with other approaches as well, which is shown in Table 3. We want to mention that all the models listed in that table are not implemented by us. The scores shown in the table are as it was reported in the corresponding papers. The average scores of each class/tissue are the average scores of the three planes. From Table 3, we can see that our method has scored the highest DSC scores in all three tissues. If we compare with the best previous approach, as mentioned in the table, our model has performed better by 1%, 2%, and 1% in WM, GM, and CSF, respectively. The only comparable result was obtained using patch-wise models [34; 35]. Therefore, we can conclude that our method has performed better than the previous state-of-the-art approaches for brain tissue segmentation. Although the alternative update of the model is time-consuming, our proposed algorithm improved the DSC score up to 2% more than the model with DSC alone as a loss function. While comparing the single update of the model and the alternative update, we found that the alternative update outperformed in the axial and coronal planes. The scores are improved by up to 1%.