# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## 1 Introduction  
Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates through clinical interpretation and volumetric analyses (e.g., intracranial volume (ICV) and regional brain volume measurements) [1; 2]. In this context, image acquisition and assessment pose unique challenges [2; 3; 4]: (1) fast imaging sequences, with the incomplete filling of the _k-space_, are typically used in order to avoid sedation and movement artifacts, but at the expense of image quality; (2) the use of two-dimensional (2D) sequences with thick slices to further decrease the acquisition time leads to partial volume effects which may compromise the assessment of the small brain structures being imaged; (3) the contrast-to-noise ratio (CNR) between grey and white matter may be lower if imaging sequences are not optimized regarding repetition and echo times; (4) if dedicated coils are not available, the signal-to-noise ratio (SNR) might also be compromised; (5) the appearance of the brain and regional structures differs from the adult brain and undergoes rapid changes with gestational age (GA) and postnatal maturation (due to evolving myelination, decreases in brain water content, and an increase in tissue density); (6) abnormalities may be related to specific perinatal pathologies that are nottypically observed in adults and usually evolve rapidly; and (7) image processing methods also vary across premature newborns, infants, and adults.  
As an essential pre-processing step for brain tissue segmentation and image registration, brain extraction (BE) or skull stripping is necessary to remove non-brain tissue [5; 6; 7]. This is a critical step because inaccurate intracranial segmentation, e.g., unremoved non-brain tissues or incorrectly removed brain tissues, could result in the under- or over-estimation of brain volumes [5].  
There are several methods for BE (i.e., manual, semi-automated, and automated algorithms), but manual segmentation is still considered the gold standard or ground truth for BE; despite being time consuming and having inter/intra-rater variability, they are often used to validate semi-automatic and automatic methods [6; 7; 8; 9; 10; 11]. These methods can be classified as follows: conventional methods (deformable surface-based, mathematical morphology, intensity, template, and hybrid-based models) as well as machine learning and deep learning methods [7; 9; 11]. The majority of the available methods are focused on the adult brain (e.g., BEAST [12], BEMA [13], BET [8], BET2 [14], BSE [15], CONSNet [16], DMBE [17], FSW [18], HD-BET [19], MASS [20], McStrip [21], ROBEX [22], MONSTR [23], SBA [24], SPECTRE [25], SynthStrip [26], 3 dSKullStrip [27], and the 3D U-Net approach [28]), but some have been optimized or developed specifically for neonates (e.g., ALFA [29], iBEAT2 [30], LABEL [31], STAPLE [32], GCSP [10], HSS [33], AFSS [34], and the fuzzy object model-based fuzzy connectedness approach [35]).  
BE remains challenging in neonatal brain MRI because of the low spatial resolution, low signal-to-noise ratio, low contrast-to-noise ratio, wide variation in intensity within tissues, small brain size, evolving shape, and motion artifacts when compared with adult brain images [5; 6; 11]. Additionally, most BE methods are designed to work with T1-weighted (T1w) MRI, but in neonates, these images lack contrast between brain tissues, so T2-weighted (T2w) images are most often used [1; 2; 36].  
Therefore, the purpose of this study was to apply different BE methods in the MRI of preterm neonates and assess their performances, focusing on comparisons with manual segmentation as well as on the effect they have in estimating ICVs.