# 3D U-Net for Skull Stripping in Brain MRI
## 3 Experimental Results and Discussion
### Qualitative Results.  
For the qualitative performance of the proposed method and comparison with other algorithms, some orthogonal slices from the test data are depicted in Figure 4-7. The first and second column of the figure displays input and ground truth orthogonal slices, from different scans, respectively. Last three columns show brain extraction results from the 3D-UNet, BSE, and ROBEX, respectively. One important aspect of both the algorithms (ROBEX and BSE) is that their segmentation in the inferior (eyes images) and superior slices are not good, and show mediocre performance as shown in Figures 4 and 5. The failure region (under or over-segmentation of the brain) have been indicated with a rectangle and displayed in a zoom view. The 3D-UNet produces more accurate and smooth generation of brain compared to BSE and ROBEX.  
Figure 3: Dice comparison of 3D-UNet with BSE, ROBEX and [PERSON]’s method on a single test brain MRI scan slice-by-slice. BSE and ROBEX failed to correctly segment the brain in the initial slices of the brain MRI volume. The Dice score in the slices (from 80–140 slices) for both the competitor algorithms is less than 0.90 (on average).  
ROBEX shows improved performance without any parameter tuning. One disadvantage of the ROBEX is that it smooths gyri and sulci (contour of the brain) excessively that lead to the inclusion of non-brain tissue dura and/or gray matter loss, as represented in Figure 4. BSE also failed when no significant difference was present between the intensities of the brain and non-brain edges. Furthermore, the skull-stripping results of both the algorithms are not consistent. ROBEX had a high sensitivity but poor Dice coefficient (overlap) and specificity. Similarly, BSE had greater score of specificity but low sensitivity and Dice coefficient values. On the other hand, the brain extraction results of both CNN-based algorithms are excellent and consistent in either case, i.e., the brain slices with eyes or without eyes, as shown in Figure 6. BSE is the fastest among the compared algorithms. It took only a 3.5(\(\pm\)0.4) s for the whole brain extraction. 3D-UNet and ROBEX spent 228(\(\pm\)0.5) s and 73.8(\(\pm\)1.6) s, respectively. 3D-UNet takes more time in skull-stripping due to deep CNNs structure, but its good performance and results are worth considering, and using it for skull-stripping. Figure 7 shows one result example of qualitative comparison of 3D U-Net and [PERSON]'s methods. Both deep learning-based methods perform outstandingly well, showing little difference with ground truth.  
Figure 4: Skull stripping result on brain image of the eyes. 3D-UNet produced more accurate and smooth segmentation of the brain as compared to BSE and ROBEX. Segmentation failure regions (under or over-segmentation of the brain) are indicated with the rectangles, along with their zoom view.  
Figure 5: Qualitative performance of the 3D-UNet with BSE and ROBEX. Segmentation failure regions (under or over-segmentation of the brain) are indicated with the rectangles, along with their zoom view.  
Figure 6: Qualitative performance comparison of the proposed method. Small rectangles indicate segmentation failure produced by BSE and ROBEX.