## 4 Discussion
Assessing them separately for each BE, better results are observed when using N4 ITK than SPM-BFC, i.e., less statistically significant differences between the RBVs obtained when applying the different BE methods. Concerning the influence of BE methods in neonatal brain MRI, SWS and HD-BET, when bias-corrected with N4 ITK, only show differences on CB and BS volumes; if segmentation of the cerebrum's tissue volumes is intended, then both BE methods can be used interchangeably without compromising the results. Regarding the other two automated BE methods (BET2 and SynthStrip), careful interpretation should be done of the obtained volumes, as results consider the total volume of the segmented region, without considering the spatial overlap of those regions separately, so having a comparable brain tissue volume (i.e., without statistically significant differences) may not be correlated with the similarity level between the tissue segmentation [78; 79; 10]. Consequently, the choice of the BE method impacts subsequent brain tissue segmentation, as demonstrated by small differences in Dice resulting in significant differences in the following processes for brain analysis. The selection of the preprocessing methods (BE and BFC) has a direct impact on the volumetric measurements, potentially affecting clinical interpretations. The accuracy of RBV data complements the neuroimaging phenotype and might have an influence on clinical decision-making (e.g., detecting subtle brain abnormalities, assessing the effectiveness of intervention, optimizing brain growth, predicting neurodevelopmental outcomes) [7; 8; 9]. However, the prognostic value of the results remains limited because the reference values are not yet established, and also the partial volume effect associated with the neonatal MRI resolution may compromise the assessment of the small brain structures, emphasizing the need for continued research and refinement of the image processing methods. In this work, the limitations are also related to the small sample size and the biased selection of subjects without major brain lesions. The reasons for this are twofold: (1) the evaluation of automated BE methods typically requires manual segmentation as a reference standard, and this process is highly time-consuming, which explains why many studies in the field have relied on small sample sizes (ranging from 5 to 22 subjects) [80; 81; 82; 83]; and (2) it is recommended to initially test the impact of image processing methods on subjects without detectable brain lesions, congenital conditions, or severe motion artifacts [84; 85; 83; 52]. These factors combined to constrain the size of our study sample, reflecting a common challenge in this area of research. The fact that the dataset was collected with one scanner at one center ensures consistency in data acquisition and image features, which is important when assessing the impact of different preprocessing methods. However, it might limit the generalizability of the results to other scanners, namely, in MRI scanners with different magnetic field strengths (e.g., higher field strengths have several advantages, such as higher spatial resolution, contrast and signal-to-noise ratio, but with a drawback of increasing the intensity inhomogeneities [86]). In this study we did not consider the ethnicity, race, or gender as a variable; however, some studies reported neonatal brain morphological variability [87; 88; 89] and others even defined population-specific templates [90] to be used. Despite the brain volume differences that might exist intrinsic to those variables, we are unsure how it affects the impact of the preprocessing methods. Finally, the application of BFC methods should be carefully interpreted in cases with motion artifacts and pathological lesions, since it was reported [91] in adults with significant white matter pathology that BFC might distort the real hyperintensities, although with the overall volumetric change not being significantly affected.