###### Abstract  
Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates' T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4 ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4 ITK, showed the highest performance (mean \(\pm\) standard deviation) regarding CV of 0.047 \(\pm\) 0.005 (white matter) and 0.070 \(\pm\) 0.005 (grey matter), CJV of 0.662 \(\pm\) 0.095, DC of 0.942 \(\pm\) 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA. bias field correction; brain extraction; neonatal MRI; regional brain volume; segmentation Article Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction with In Neonatal MRI  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction with In Neonatal MRI  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction with In Situ Cardiac Editors  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Several studies have described associations between RBVs at TEA and early/long-term neurodevelopmental impairment (e.g., motor, sensory, cognitive, behavioral) [7; 9; 12; 13; 14; 15], highlighting the importance of studying and optimizing methods for better RBV quantification. Some image preprocessing steps are essential for subsequent brain tissue segmentation, namely, brain extraction (BE) or skull-stripping and bias field correction (BFC), also known as intensity inhomogeneity/nonuniformity correction [16; 17]. Intracranial segmentation occurs prior to regional segmentation, and is of the greatest importance as it can significantly impact the estimation of volumes, especially of the tissues close to the edges of the brain contour that can become under- or overestimated depending on the BE performance when removing non-brain tissues [10; 16]. Several approaches can be used for BE in the neonatal brain, ranging from conventional (deformable surface-based, mathematical morphology, intensity, template, and hybrid-based models) to deep learning methods [18; 19; 20]. As such, selection of a proper BE method is warranted [10]. Additionally, correction of intensity inhomogeneities in MRI is important because it can also affect quantitative image analysis. Intensity inhomogeneity in MRI is characterized by a gradual and smooth variation in intensity across the image, leading to different intensities for the same tissue depending on the region, and is dependent on the strength of the magnetic field [21].