# 3D U-Net for Skull Stripping in Brain MRI
## 3 Experimental Results and Discussion
### Quantitative Results  
Table 2 summarizes the overall analysis for each evaluated metric i.e., Dice, sensitivity, and specificity, respectively. The plot of these measurements for the individual test scan is illustrated in Figure 2. Performance of the 3D-UNet method was excellent with respect to Dice, sensitivity, and specificity, across all the test datasets; the conventional methods (BSE and ROBEX) generally performed well (i.e., > 0.9 on Dice coefficient, sensitivity, and specificity). BSE shows a better Dice score as compared to ROBEX, particularly, the Dice score for scan 5 is substantially different (0.8793) as depicted in Figure 2a. The overall sensitivity values of ROBEX for test scans is relatively greater than BSE. This indicates that ROBEX classified the brain tissues more accurately and segmented most of the brain because the sensitivity assesses how much brain tissue is included in the brain segmentation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject I.D.**} & \multicolumn{2}{c}{**Dice Coefficient**} & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} \\ \cline{2-10}  & **BSE** & **ROBEX** & **3D** & **BSE** & **ROBEX** & **3D** & **BSE** & **ROBEX** & **3D** \\  & **BSE** & **ROBEX** & **UNet** & **UNet** & **UNet** & **UNet** \\ \hline A00061276 & 0.9435 & 0.9568 & 0.9908 & 0.8883 & 0.9413 & 0.9853 & 0.9929 & 0.9710 & 0.9962 \\ A00061387 & 0.9547 & 0.9431 & 0.9911 & 0.9186 & 0.9530 & 0.9865 & 0.9876 & 0.9342 & 0.9956 \\ A00061709 & 0.9583 & 0.9528 & 0.9888 & 0.9306 & 0.9512 & 0.9890 & 0.9838 & 0.9543 & 0.9885 \\ A00061711 & 0.9663 & 0.9467 & 0.9896 & 0.9435 & 0.9935 & 0.9856 & 0.9876 & 0.9531 & 0.9935 \\ A00061806 & 0.9637 & 0.8793 & 0.9876 & 0.9913 & 0.8851 & 0.9797 & 0.9939 & 0.9015 & 0.9953 \\ A00062210 & 0.964 & 0.9508 & 0.9913 & 0.9336 & 0.9216 & 0.9891 & 0.9923 & 0.9774 & 0.9934 \\ A00062248 & 0.9585 & 0.9579 & 0.9914 & 0.9208 & 0.9434 & 0.9858 & 0.9933 & 0.9713 & 0.9969 \\ A00062266 & 0.9482 & 0.9546 & 0.9915 & 0.8919 & 0.9521 & 0.9863 & 0.9989 & 0.9569 & 0.9966 \\ A00062282 & 0.9587 & 0.9466 & 0.9897 & 0.9314 & 0.9519 & 0.9826 & 0.9838 & 0.9417 & 0.9967 \\ A00062288 & 0.9674 & 0.9473 & 0.9905 & 0.9438 & 0.9397 & 0.9842 & 0.9895 & 0.9542 & 0.9968 \\ A00062351 & 0.9554 & 0.9488 & 0.9909 & 0.9235 & 0.9928 & 0.9907 & 0.9846 & 0.9633 & 0.9912 \\ A00062917 & 0.9557 & 0.9549 & 0.9897 & 0.9278 & 0.9338 & 0.9839 & 0.9813 & 0.9741 & 0.9954 \\ A00062934 & 0.9197 & 0.9451 & 0.9921 & 0.8329 & 0.9409 & 0.9887 & 0.9935 & 0.9487 & 0.9954 \\ A00062942 & 0.9608 & 0.9507 & 0.9922 & 0.9255 & 0.9164 & 0.9877 & 0.9962 & 0.9817 & 0.9966 \\ A0006308 & 0.9705 & 0.9588 & 0.9915 & 0.9437 & 0.9334 & 0.9863 & 0.9957 & 0.9823 & 0.9947 \\ A00063103 & 0.9544 & 0.9602 & 0.9884 & 0.9116 & 0.9434 & 0.9794 & 0.9935 & 0.9757 & 0.9972 \\ A00063326 & 0.9436 & 0.9503 & 0.9896 & 0.8956 & 0.9372 & 0.9811 & 0.9864 & 0.9621 & 0.9979 \\ A00063368 & 0.953 & 0.9478 & 0.9861 & 0.9030 & 0.9370 & 0.9751 & 0.9985 & 0.9575 & 0.9967 \\ A00063589 & 0.9607 & 0.9512 & 0.9916 & 0.9349 & 0.9440 & 0.9877 & 0.9846 & 0.9577 & 0.9956 \\ A00064081 & 0.9647 & 0.9511 & 0.9923 & 0.9296 & 0.9209 & 0.9888 & 0.9974 & 0.9785 & 0.9958 \\
**Mean** & 0.9561 & 0.9477 & 0.9903 & 0.9179 & 0.9342 & 0.9853 & 0.9908 & 0.9999 & 0.9953 \\
**Standard deviation** & 0.0113 & 0.0168 & 0.0016 & 0.0260 & 0.0221 & 0.0040 & 0.0055 & 0.0192 & 0.0022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall quantitative analysis and comparison of 3D-UNet with Brain Surface Extractor (BSE) and Robust Brain Extraction (ROBEX) against manual segmentation results for the test brain MRIs datasets. The best values are emboldened.