## 3 Results
### Segmentation Performance of Conventional Models  
The final segmentation rates obtained using the conventional models are summarized in terms of segmentation accuracy, sensitivity, specificity, Dice score coefficient, and AUC (Table 2, Figure 2). Our model's dice score in scanner-specific configuration on Dataset-I, Dataset-II, and Dataset-III was 80.23%, 81.43%, and 79.12% for linear kernel; 81.34%, 82.65%, and 81.23% for RBF kernel; and 79.23%, 80.43%, and 80.54% for sigmoid kernel, respectively, on handcrafted features. The cross-validated dice score with linear, RBF, and sigmoid kernels was 78.98%, 78.98%, and 77.34%, respectively, when data from different institutions was pooled together (scanner-agnostic configuration).  
Moreover, the model yielded dice score of 82.34%, 81.76%, and 82.54% for linear kernel, 80.54%, 81.43%, and 80.12% for RBF kernel, and 81.45%, 82.76%, and 81.67% for sigmoid kernel, respectively, on Dataset-I, Dataset-II, and Dataset-III by using transfer learning features in scanner-specific configuration. Similarly, the cross-validated dice score using transfer learning features with linear, RBF and sigmoid kernels were 78.23%, 79.76%, and 79.43% in scanner-agnostic configuration. No statistically significant differences of the classification methods among the given datasets by McN  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Performance Measures**} & \multicolumn{3}{c}{**Linear**} & \multicolumn{3}{c}{**RBF**} & \multicolumn{3}{c}{**Sigmoid**} \\ \hline \multicolumn{2}{c}{} & \multicolumn{6}{c}{**Handcrafted Features**} \\ \hline \multicolumn{2}{c}{} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} & \multicolumn{2}{c}{**Scanner-**} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{**Specific**} & \multicolumn{2}{c}{**Agnostic**} & \multicolumn{2}{c}{**Specific**} & \multicolumn{2}{c}{**Agnostic**} & \multicolumn{2}{c}{**Specific**} & \multicolumn{2}{c}{**Agnostic**} \\ \hline \multicolumn{2}{c}{} & **I** & **II** & **III** & **Combined** & **I** & **II** & **III** & **Combined** & **I** & **III** & **Combined** \\ \hline Dice score & 80.23 & 81.43 & 79.12 & 78.98 & 81.34 & 82.65 & 81.23 & 78.98 & 79.23 & 80.43 & 80.54 & 77.34 \\ Accuracy & 78.21 & 81.32 & 79.87 & 77.45 & 81.76 & 81.34 & 79.23 & 77.23 & 77.12 & 80.12 & 79.34 & 76.32 \\ Sensitivity & 82.43 & 78.23 & 80.12 & 79.45 & 78.43 & 83.32 & 82.43 & 80.32 & 80.54 & 79.65 & 81.54 & 79.12 \\ Specificity & 83.12 & 80.78 & 81.90 & 78.43 & 80.43 & 80.56 & 80.56 & 79.23 & 77.32 & 81.87 & 80.87 & 76.32 \\ AUC & 0.810 & 0.801 & 0.805 & 0.771 & 0.811 & 0.807 & 0.799 & 0.771 & 0.721 & 0.791 & 0.786 & 0.789 \\ \hline \multicolumn{2}{c}{} & \multicolumn{6}{c}{**Transfer learning Features**} \\ \hline \multicolumn{2}{c}{} & **I** & **II** & **III** & **Combined** & **I** & **II** & **III** & **Combined** & **I** & **II** & **III** & **Combined** \\ \hline Dice score & 82.34 & 81.76 & 82.54 & 78.23 & 80.54 & 81.43 & 80.12 & 79.76 & 81.45 & 82.76 & 81.67 & 79.43 \\ Accuracy & 83.32 & 82.65 & 81.65 & 79.56 & 81.56 & 80.23 & 80.54 & 76.23 & 82.43 & 81.67 & 80.43 & 76.45 \\ Sensitivity & 81.42 & 81.76 & 80.78 & 80.23 & 75.56 & 79.43 & 79.67 & 79.43 & 80.54 & 79.32 & 80.76 & 79.43 \\ Specificity & 81.65 & 82.87 & 81.23 & 77.43 & 78.43 & 81.45 & 81.98 & 78.12 & 81.98 & 81.65 & 81.78 & 77.43 \\ AUC & 0.823 & 0.829 & 0.816 & 0.812 & 0.797 & 0.812 & 0.814 & 0.789 & 0.818 & 0.823 & 0.812 & 0.796 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of conventional models on handcrafted and transfer learning features in scanner-specific and scanner-agnostic configurations. I, II, and III correspond to Dataset-I, Dataset-II, and Dataset-III.  
Figure 2: Representative segmentation results of the proposed method. Rows show different subjects, whereas columns show data from different institutions.