# 3D U-Net for Skull Stripping in Brain MRI
## 3 Experimental Results and Discussion
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mean \& Standard Deviation** & **Dice Coefficient** & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} \\ \hline \multirow{2}{*}{**Method**} & **[PERSON]** & **3D** & **[PERSON]** & **3D** & **[PERSON]** & **3D** \\  & **UNet** & **UNet** & **UNet** & **UNet** & **UNet** \\ \hline
**Mean** & **0.9950** & 0.9903 & 0.9745 & **0.9853** & **0.9973** & 0.9953 \\
**Std.** & **0.0008** & 0.0016 & 0.0063 & **0.0040** & **0.0007** & 0.0022 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of mean and standard deviation for [PERSON]’s method and 3D U-Net. The best values are emboldened. Figure 2: Quantitative comparison of 3D-UNet with BSE, ROBEX and [PERSON]’s method on test scans from NFBS database of brain MRIs. 3D-UNet shows more consistent results as compared to BSE and ROBEX. (**a**) Kleeseik’s Method shows the highest performance. BSE and ROBEX have large fluctuations. (**b**) 3D-UNet has the highest performance. As in [PERSON]’s standard, BSE and ROBEX are highly volatile. (**c**) Kleeseik’s Method has the highest performance and ROBEX has the lowest performance. On the other hand, the specificity scores of BSE is much better than the ROBEX. This represents that BSE segmented the non-brain tissues more correctly and excluded most of the non-brain region compared to ROBEX. Moreover, BSE and ROBEX failed to correctly segment the brain in the initial slices of the brain MRI volume. The Dice score in the slices (40-45% of the total slices contains brain tissues) for both the competitor algorithms is less than 0.90 (on average) as displayed in Figure 3. In contrary, the 3D-UNet shows a consistent performance in all the slices either from the start or at the end of the brain MRI volume. Figure 3 portrayed the slice-by-slice Dice score comparison of the individual algorithm with 3D-UNet. The overall quantitative performance of the 3D-UNet with ROBEX and BSE is shown in Table 2. Best values (results) in the respective category are emboldened. Figure 2 demonstrates corresponding plots for each algorithm, including all the test brain MRI scans. The proposed algorithm achieved the highest average Dice, sensitivity, and specificity score with a significant difference when compared with BSE and ROBEX. Sensitivity value for BSE is smaller than the other methods and indicates that it has excluded several brain tissues than the other methods. Specificity value for ROBEX is substantially small for test dataset (scan 5) and implies that it has included more non-brain tissues than the other methods. Table 3 shows the comparison results of [PERSON]'s method and 3D-UNet. Both deep learning-based methods show excellent performance compared to classical methods. [PERSON]'s method seems to be a little superior in terms of dice coefficient and specificity. The sensitivity value of 3D-UNet was a little better as compared to [PERSON]'s algorithm. In conclusion, the results of both the CNNs-based methods are comparable and outperformed the classical best skull stripping algorithms.