## 3 Results
### Effect of BET Parameter Configurations  
The best-performing BET pipeline in terms of the mean DSC was BET \(|\)B - N3 iter = 1 for T1-MPRAGE and BET \(|\)B,f0.2,g0.3 - N3 iter = 4 for T2-FLAIR images. While previous investigations into BET parameter optimization have found that BET \(|\) B,f0.1 has outstanding performance in the presence of other kinds of pathology, as shown in Figure 2, we found that BET \(|\) B,f0.1 performs worse than BET \(|\) B, BET \(|\) R, and BET \(|\) B,f0.2,g0.3 for T1-MPRAGE MRIs, regardless of the intensity processing procedure [36]. The top BET \(|\)B pipeline (BET \(|\)B - N3 iter = 1) resulted in a mean DSC score of 0.9391, the mean DSC score of the top BET \(|\)R pipeline (BET \(|\)R - N3 iter = 4) was 0.9383, and the top BET \(|\)B,f0.2,g0.3 pipeline (BET \(|\)B,f0.2,g0.3 - N3 iter = 1) resulted in a mean DSC score of 0.9262, while the top BET \(|\)B,f0.1 pipeline (BET \(|\)B,f0.1 - N3 iter = 1) had a mean DSC of 0.9142 (differences were \(p<0.001\)). In contrast, BET \(|\) B,f0.1 performed markedly better in T2-FLAIR. The top BET \(|\) B,f0.1 pipeline, BET \(|\) B,f0.1 - N3 iter = 4, produced the third highest DSC score among all BET pipelines for T2-FLAIR images, with a mean DSC = 0.9342. However, BET \(|\) B,f0.2,g0.3 - N3 iter = 4 and BET \(|\) R - N3 iter = 1 both still perform marginally better, producing DSC scores of 0.9384 and 0.9343, respectively. Another notable difference in pipeline performance between T1 MPRAGE and T2 FLAIR is seen in BET \(|\) B, which performs significantly worse with T2-FLAIR images. As Figure 3 shows, BET \(|\) B is the only BET configuration with a primary option (\"B\" or \"R\") to perform worse than pipelines without a primary option. Specifically, the top default BET pipeline (BET - ZS m = BET \(|\) f0.8) and the top BET \(|\) g\(-\)0.3 pipeline (BET \(|\)g\(-\)0.3 - KDE m = BET \(|\) f0.1) had mean DSC scores of 0.9197 and 0.9051, respectively, while the highest-performing BET \(|\) B pipeline (BET \(|\)B - ZS iter = 1) had a mean DSC score of only 0.8963. For both MRI types, default BET and BET \(|\) g\(-\)0.3 had distinctly better performance relative to other BET configurations with no primary option--namely, BET \(|\) g0.3, BET \(|\) f0.8, and BET \(|\) f0.2,g0.3. The highest-performing pipelines using default BET were BET - N3 iter = 4 for T1-MPRAGE images, with a mean DSC of 0.9097, and BET - ZS iter = 4 for T2-FLAIR images, with a mean DSC of 0.9193. For BET \(|\) g\(-\)0.3, the highest-performing pipelines were BET \(|\) g\(-\)0.3 - N3 iter = 4 for T1-MPRAGE images and BET \(|\) g\(-\)0.3 - KDE m = BET \(|\) f0.1 for T2-FLAIR images, which had mean DSC scores of 0.9029 and 0.9051, respectively. On the other hand, BET \(|\) g0.3, BET \(|\) f0.8, and BET \(|\) f0.2,g0.3 exhibited worse performance. In Table 3, the top BET \(|\) f0.2 marks the second point of discontinuity for T1-MPRAGE images and the first major point of discontinuity for T2-FLAIR MRIs, representing a drastic decrease in pipeline performance from the preceding pipelines for both MRI sequences. For T1-MPRAGE, the poorest-performing default BET pipeline, BET - KDE m=BET \(|\) f0.8, has a mean DSC score of 0.8964 and precedes the top BET \(|\) f0.2 pipeline, BET \(|\) f0.2 - ZS iter = 4, in which the mean DSC plummets to 0.7776. This discontinuity can also be observed in the difference between the slopes of the pipelines for each extraction configuration in Figure 2.  
An analogous but less drastic drop in mean DSC scores occurs at this point for T2-FLAIR images as well, where the lowest-performing BET \(|\) B pipeline (BET \(|\)B - KDE m = BET \(|\) f0.8) precedes the top BET \(|\) f0.2 pipeline (BET \(|\) f0.2 - ZS iter = 4), with the mean DSC decreasing from 0.8604 to 0.8361. In contrast to the T1-MPRAGE results, this was not the largest point of discontinuity for the T2-FLAIR images.