## 3 Results
### Effect of Intensity Processing Procedures
#### 3.2.2 Intensity Normalization  
In comparison to N3 inhomogeneity correction, Z-score, KDE-based, and WhiteStripe intensity normalization demonstrated more idiosyncratic effects on the pipelines for T1-MPRAGE MRIs. However, significant positive differences were also observed. KDE-based normalization with m = BET 1 f0.5 increased the DSC for BET 1 f0.2,g0.3, BET 1 f0.2, and BET 1 g-0.3 configurations (\(p<0.05\)) (Figure 4). Additionally, KDE m = BET 1 f0.8 increased the DSC for BET 1 f0.2,g0.3 (\(p<0.05\)). WhiteStripe normalization with m = BET 1 f0.1 increased the DSC for BET 1 B and BET 1 f0.2,g0.3 (\(p<0.001\) and \(p<0.05\), respectively). WS m = BET 1 f0.5 increased the DSC for BET 1 f0.2,g0.3, BET 1 f0.2, BET 1 R, and BET, with sig nificance ranging from \(p<0.05\) to \(p<0.01\). WS m = BET! f0.8 increased the DSC for BET! f0.2 and BET! f0.8 (\(p<0.05\)). Z-score normalization with ZS iter = 1 increased the DSC for BET! g-0.3 (\(p<0.05\)). ZS iter = 4 increased the DSC for BET! f0.2,g0.3, \(p<0.05\). ZS m = BET! f0.1 increased the DSC for BET! f0.8, BET! B, and BET! f0.2,g0.3, with significance from \(p<0.05\) to \(p<0.01\). Lastly, ZS m = BET! f0.8 increased the DSC for BET! B and BET! f0.2,g0.3, with \(p<0.01\) and \(p<0.05\), respectively (Figure 4). Negative effects were also observed with different normalization procedures in T1-MPRAGE MRIs. KDE m = BET! f0.1 resulted in a decrease in DSC for BET! B (\(p<0.001\)), BET! B,f0.2,g0.3 (\(p<0.01\)), and optiBET (\(p<0.05\)). Additionally, KDE m = BET! f0.5 led to a decrease in DSC for BET! B (\(p<0.01\)) and optiBET (\(p<0.02\)), while KDE m = BET! f0.8 caused a decrease in DSC for BET (\(p<0.001\)), BET! B (\(p<0.01\)), and optiBET (\(p<0.02\)) (Figure 4). WhiteStripe normalization also showed detrimental effects; WS m = BET! f0.1 resulted in a decrease in DSC for optiBET (\(p<0.001\)) and BET! B (\(p<0.001\)). WS m = BET! f0.5 led to a decrease in DSC for BET! B (\(p<0.001\)), optiBET (\(p<0.001\)), BET! B,f0.1 (\(p<0.01\)), and BET! g0.3 (\(p<0.05\)). Z-score normalization was not exempt from negative impacts either; ZS iter = 1 resulted in a decrease in DSC for optiBET (\(p<0.001\)) and BET! B,f0.2,g0.3 (\(p<0.01\)). ZS iter = 4 led to a decrease in DSC for optiBET (\(p<0.001\)) and BET! g0.3 (\(p<0.05\)). Additionally, ZS m = BET! f0.1 caused a decrease in DSC for optiBET (\(p<0.001\)), BET! B,f0.2,g0.3 (\(p<0.01\)), and BET (\(p<0.05\)), while ZS m = BET! f0.5 and ZS m = BET! f0.8 each led to a decrease in DSC for BET! g0.3 (\(p<0.05\)) (Figure 4). For T2-FLAIR MRIs, there were fewer significant positive effects. KDE m = BET! f0.8 demonstrated an increase in DSC with a \(p\)-value of less than 0.05. For the BET! g-0.3 configuration, an increase was also observed with KDE m = BET! f0.1, showing a significant improvement (\(p<0.001\)) (Figure 5). In the BET! f0 configuration, several normalization techniques resulted in notable increases in DSC: WS m = BET! f0.1 and WS m = BET! f0.5 both achieved increases with \(p<0.001\) and WS m = BET! f0.8 with \(p<0.01\). Additionally, ZS iter = 1 and ZS iter = 4 in the BET! B configuration led to significant increases in DSC, both \(p<0.001\). Further enhancements in the same configuration were seen with ZS m = BET! f0.8, ZS m = BET! f0.5, and ZS m = BET! f0.1 (all \(p<0.01\)) (Figure 5). Negative effects were also observed across different procedures for the T2-FLAIR images. In the BET! B configuration, KDE m = BET! f0.8 and KDE m = BET! f0.5 both led to a decrease in DSC, each with (\(p<0.05\)) (Figure 5). Similarly, in the BET! f0.2,g0.3 configuration, KDE m = BET! f0.1 and KDE m = BET! f0.5 resulted in decreases in DSC with \(p<0.01\) and \(p<0.05\), respectively. Moreover, KDE m = BET! f0.5 in the BET! g-0.3 configuration showed a significant decrease in DSC (\(p<0.001\)). WhiteStripe normalization also demonstrated detrimental effects; in the BET! f0.2,g0.3 configuration, WS m = BET! f0.1 and WS m = BET! f0.5 both resulted in decreases in DSC, with \(p<0.01\) and \(p<0.05\), respectively.