## 3 Results  
### Assessment of Intensity Variability and Segmentation Performance  
Exemplary results obtained before and after application of BFC methods on T2w images from the dataset are shown in Figure 1. Visually, it is possible to identify variations in intensity (Figure 1c,d) after different bias corrections, but is hard to visually perceive changes after the respective brain tissue segmentations (Figure 2), as these are quite discreet.  
Quantitative assessments regarding the intensity variability of brain tissues are shown in Table 1 and Figure 3, and regarding segmentation performance in Figure 4.  
Figure 1: Intensity inhomogeneity on TEA brain T2w MRI preterm neonate: (**a**) skull-stripped MRI without correction; (**b**) bias field map; bias-corrected MRI with (**c**) N4 ITK and (**d**) SPM-BFC methods; (images displayed with the Jet color map to ease the comparison between corrections).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Measure**} & \multirow{2}{*}{**Brain Extraction Method**} & \multicolumn{3}{c}{**Bias Field Correction Method**} \\ \cline{3-5}  & & **Meth** & **Uncorrected** & **SPM-BFC** & **N4 ITK** \\ \hline \multirow{4}{*}{CV (WM)} & Manual & 0.056 \(\pm\) 0.009 & 0.057 \(\pm\) 0.011 & 0.048 \(\pm\) 0.006 \\ \cline{2-5}  & BET2 & 0.056 \(\pm\) 0.009 & 0.058 \(\pm\) 0.011 & 0.048 \(\pm\) 0.005 \\ \cline{2-5}  & SWS & 0.055 \(\pm\) 0.009 & 0.057 \(\pm\) 0.011 & 0.047 \(\pm\) 0.006 \\ \cline{2-5}  & HD-BET & 0.056 \(\pm\) 0.009 & 0.058 \(\pm\) 0.011 & 0.047 \(\pm\) 0.005 \\ \cline{2-5}  & SynthStrip & 0.056 \(\pm\) 0.009 & 0.057 \(\pm\) 0.011 & 0.048 \(\pm\) 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficient of variation (CV) of WM and GM and coefficient of joint variation (CJV), for uncorrected and bias field-corrected images after applying different brain extraction methods.  
Figure 2: TEA brain T2w MRI (manually skull-stripped) of a preterm neonate in three different axial slices bias-corrected with SPM-BFC (**a–c**) and N4 ITK (**d–f**), with overlaid brain tissues/structures segmented using MANTiS (color coded: CGM, red; WM, green; CSF, blue; DNGM, yellow; Hip, light blue; Amy, pink; CB, grey; BS, dark blue).  
Figure 4: Dice coefficient (DC) based on the regional brain tissue segmentation maps obtained from MRI uncorrected and bias field-corrected using SPM-BFC or N4 ITK, for each brain extraction method (Manual, BET2, SWS, HD-BET, and SynthStrip).  
Figure 3: Coefficient of joint variation (CJV) between white and grey matter intensities from MRI uncorrected and bias field-corrected with SPM-BFC and N4 ITK, for each brain extraction method (Manual, BET2, SWS, HD-BET, and SynthStrip).  
From Table 1 and Figure 3, it is observed that the CV (WM), CV (GM), and CJV (WM,GM) were reduced after BFC using N4 ITK, but not using SPM-BFC, compared with the uncorrected images. The decrease in the CJV values after BFC using N4 ITK indicates the improved separation between different region intensities, more specifically when HD-BET (0.662 \(\pm\) 0.095) was used as the BE method. Additionally, the segmentation performance was assessed, and according to Figure 4, the highest similarity levels between the segmentation maps were obtained when using SWS (DC of 0.940 \(\pm\) 0.060) or HD-BET (DC of 0.942 \(\pm\) 0.063) as BE methods, and the lowest similarity levels when using BET2 (DC of 0.779 \(\pm\) 0.144) or SynthStrip (DC of 0.713 \(\pm\) 0.056), with a similar behavior for all uncorrected or bias field-corrected images.  
Quantitative assessments showed differences between BFC methods regarding intensity variability (Figure 3), but those were not as evident on the segmentation performance (Figure 4). As such, the impact of BFC for each RBV was assessed.