# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## 4 Discussion
Skull stripping, as a preprocessing step, should be fast, providing results that are as accurate as those of manual segmentation, which nonetheless is extremely time consuming when compared with automated methods ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). Herein, we have observed that conventional automated BE methods perform faster than deep-learning-based methods, in agreement with the computation times described by other authors ([PERSON] et al., 2016; [PERSON] et al., 2016). However, this limitation of the latter methods could be overcome by running them in GPU mode instead of CPU mode (e.g., HD-BET takes approximately 30 s in GPU mode ([PERSON] et al., 2018)). This study achieves the following: (1) it determines the applicability of state-of-the-art BE methods, such as HD-BET and SynthStrip, in MRI preterm infants scanned at TEA; (2) validates SWS with manual segmentation; (3) optimizes the parameters for BET2 and SynthStrip to apply in neonates; and (4) culminates in a proposal of correction factors for BE automated methods. The current study also had some limitations, namely the dataset used for this study was small. This is because for testing the outputs of BE methods, it is recommended (1) to include the full manual segmentation as a reference, which is a time-consuming process and which is the main reason why the majority of similar studies have small sample sizes (e.g., several studies ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016) reported small datasets/subgroups ranging from 5 to 22 subjects); and (2) to test subjects without detectable brain lesions or congenital conditions (and no severe motion artifacts) first ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), criteria which greatly limit the available datasets, as in our case, most infants had brain abnormalities. The potential implications of these restrictions are described below:  
1. The influence of our sample size on our Bland-Altman analysis ([PERSON] et al., 2016) is difficult to determine due to the unknown clinically acceptable agreement limits for ICV measurements of preterm neonates at term. This analysis indicates the likely range of true mean differences in the specific clinical population, considering the potential for random variation ([PERSON] et al., 2016; [PERSON] et al., 2016). Nevertheless, the findings from a small sample size are less likely to capture the characteristics and variability of the overall population, mainly when statistical inferences are applied; therefore, caution is needed when generalizing conclusions. 2. The presence of brain lesions and congenital condition or motion artifacts might lead to difficulties in accurately delineating the border between the brain and the skull, depending on their severity, location (e.g., near the border of the skull or affecting skull integrity and brain tissue characteristics), and BE method category ([PERSON] et al., 2016). When using BET2 ([PERSON] et al., 2016; [PERSON] et al., 2016), which is a deformable surface-based model approach, the presence of brain pathology and motion can pose challenges in the evolution of the mesh used to approximate the brain surface, primarily due to intensity and shape variations. However, the BE methods based on deep learning techniques are known to mitigate this constraint ([PERSON] et al., 2016). For instance, [PERSON] et al. (2016) stated that HD-BET showed a robust performance in the presence of adult brain pathology- or treatment-induced tissue alterations. Additionally, [PERSON] et al. (2016) developed SynthStrip to be agnostic to MRI contrasts and acquisition schemes, and it was validated for age, health, resolution,and imaging modality factors. So, it is expected that deep learning methods behave similarly in a pathological neonatal context.