## 4 Discussion  
The present study evaluated the effect of different preprocessing steps to determine how they affect the quantification of RBVs in neonatal brain MRI, since the published studies [13; 14; 43; 44; 45; 46; 47; 48; 49; 50] were typically focused on the tissue segmentation methods, with less emphasis on the preceding BE or BFC methods. The effectiveness of the preprocessing steps for skull-stripping and removal of the bias field inhomogeneities in TEA brain T2w MRI was validated quantitatively through the analysis of intensity variability (CV and CJV), segmentation performance (DC), and volumetric (RBV) results for eight brain tissues (CGM, WM, CSF, DNGM, Hip, Amy, CB, and BS). The higher reduction in CV and CJV values in MRI bias-corrected with N4 ITK indicates reduced intensity variability (separate and joint) in both WM and GM, and lower overlap between their intensity distributions, suggesting N4 ITK as a more efficient method for neonatal brain MRI BFC than SPM-BFC. As such, the performance of SPM-BFC might be more sensitive to its regularization and bias field smoothing parameters, and default settings can be suboptimal, leading to inadequate correction, potentially increasing the CV and CJV. Other potential reasons why N4 ITK might perform better is due to its independence from segmentation and iterative approach, which may lead to more accurate corrections. It was evident for all BE methods, but particularly for skull-stripping with HD-BET, explicitly: CV (WM)--uncorrected (5.6%), SPM-BFC (5.8%), and N4 ITK (4.7%); CV (GM)--uncorrected (7.5%), SPM-BFC (7.8%), and N4 ITK (7.0%); CJV (WM,GM)--uncorrected (70.0%), SPM-BFC (71.8%), and N4 ITK (66.2%). Previous studies in adults and in simulations, using older versions of the BFC methods selected, showed similar patterns of variation. For instance, [PERSON] et al. [73] reported CV (WM)--uncorrected (6.5%), SPM2 (7.5%), and N3 (5.9%); CV (GM)--uncorrected (16.7%), SPM2 (16.0%), and N3 (16.5%); CJV (WM,GM)--uncorrected (83.9%), SPM2 (88.7%), and N3 (83.9%). Average CV for random noise and bias field  
Figure 5: TEA brain T2w MRI (example bias-corrected with N4 ITK) of a preterm neonate in three different axial slices (**a**) and corresponding skull-stripped images from each BE method (**b**) Manual, (**c**) BET2, (**d**) SWS, (**e**) HD-BET, and (**f**) SynthStrip), overlaid with segmentation from MANTiS (color coded: CGM, red; WM, green; CSF, blue; DNGM, yellow; Hip, light blue; Amy, pink; CB, grey; BS, dark blue). amplitude levels with SPM2 (3.0%) and N3 (2.5%) were described by [PERSON] et al. [74]. [PERSON] et al. [75] reported CV (WM)--SPM (6.2%) and N3 (6.0%); and CV (GM)--SPM (10.8%) and N3 (10.7%). According to [PERSON] et al. [76], studies using synthetic data resulted in CV (GM)--SPM8 (10.4%) and N3 (9.8%); CV (WM)--SPM8 (5.2%) and N3 (4.7%); CJV (WM,GM)--SPM8 (51.5%) and N3 (51.0%), and using real data resulted in CV (GM)--SPM8 (17.5%) and N3 (8.5%); CV (WM)--SPM8 (13.5%) and N3 (9.5%); CJV (WM,GM)--SPM8 (63.5%) and N3 (54.5%). Regarding DC, the BFC methods behaved similarly between them, but showed a higher overlap index of the regional segmentation maps when using SWS (DC of 0.940) and HD-BET (DC of 0.942) as BE methods. In imaging studies of term infants (0-24 months) using several BE methods (BET (a), iBEAT (b), and U-net (c)), [PERSON] et al. [77] showed the variation of average DC in brain masks (2D imaging: (a) 0.949, (b) 0.926, (a) 0.965; and 3D imaging: (a) 0.962, (b) 0.945, (a) 0.975). Additionally, in a previous study [10], the effect that BE methods had on intracranial volume measurements was tested, and the automated brain masks showed high similarity with manual segmentation (DC of 0.966 with SWS, and 0.968 with HD-BET), so a similar effect on subsequent volumetric tissue segmentation was expected. Considering brain tissue quantification, there is an influence of the bias correction method (SPM-BFC or N4 ITK) used, as shown by the significant differences mainly in the CGM, WM, CSF, and BS volumes.